Live_tweeting.ipynb

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    "colab": {
      "name": "Live tweeting.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/romellfudi/LimaTweets_NLP_TFIDF/blob/master/Live_tweeting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lyBiqUTGq-Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d43b0f44-2a44-4ffc-d703-543739b340e8"
      },
      "source": [
        "#@title @Author and building @date\n",
        "!pip install watermark\n",
        "%load_ext watermark\n",
        "from IPython.display import clear_output;clear_output()\n",
        "%watermark -a \"Romell D.Z.\" -u -d -p tweepy,scipy,nltk,gensim,sklearn,networkx,textblob,spacy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Romell D.Z. \n",
            "last updated: 2020-10-09 \n",
            "\n",
            "tweepy 3.6.0\n",
            "scipy 1.4.1\n",
            "nltk 3.2.5\n",
            "gensim 3.6.0\n",
            "sklearn 0.0\n",
            "networkx 2.5\n",
            "textblob 0.15.3\n",
            "spacy 2.2.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQe_NEVKHmlN",
        "cellView": "form"
      },
      "source": [
        "#@title Parameters\n",
        "KEYWORD='podemos' #@param {type: \"string\"}\n",
        "#@title \n",
        "NUMBER_OF_TWEETS = 250  #@param {type: \"slider\", min: 250, max: 5000, step:250}\n",
        "language = \"es\" #@param [\"es\", \"en\"] {allow-input: false}\n",
        "GEO_LOCATION_BOUNDING_BOX = [-82.1,-18.4,-68.3,-3.1] #@param {type: \"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wIAMCKgl2al",
        "cellView": "form"
      },
      "source": [
        "#@title Insert your gist which contains .netrc\n",
        "%%capture\n",
        "import os, netrc\n",
        "gist = '' #@param {type: \"string\"}\n",
        "!wget https://gist.githubusercontent.com/romellfudi/{gist}/raw/.tweet.netrc -O .netrc\n",
        "file = \".netrc\" if os.path.isfile(\".netrc\") else None\n",
        "auth = netrc.netrc(file)\n",
        "ckey,_,csecret=auth.authenticators('tweet_api')\n",
        "atoken,_,asecret=auth.authenticators('tweet_secret')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HkqhZMUGq-j"
      },
      "source": [
        "#@title Load libraries\n",
        "%%capture\n",
        "!pip install unidecode pyLDAvis pyprind networkx advertools\n",
        "!pip install deplacy\n",
        "if language == 'en':\n",
        "  !python -m spacy download en_core_web_sm\n",
        "else:\n",
        "  !python -m spacy download es_core_news_sm\n",
        "!mkdir snapshot/\n",
        "import os\n",
        "import tweepy\n",
        "from tweepy import Stream\n",
        "from unidecode import unidecode\n",
        "import re\n",
        "from tweepy import StreamListener\n",
        "from pprint import pprint\n",
        "import pyprind\n",
        "import pandas as pd\n",
        "import random\n",
        "from scipy.stats import beta as beta_distribution\n",
        "import numpy as np\n",
        "from textblob import TextBlob\n",
        "from nltk.corpus import stopwords \n",
        "from nltk import word_tokenize\n",
        "from nltk.data import load\n",
        "from nltk.stem import SnowballStemmer\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "import string\n",
        "import gensim\n",
        "from gensim import corpora\n",
        "\n",
        "import pyLDAvis\n",
        "import pyLDAvis.gensim  # don't skip this\n",
        "\n",
        "from gensim.models.ldamodel import LdaModel\n",
        "from string import punctuation\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from textblob import Word\n",
        "from textblob import TextBlob\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "import advertools as adv\n",
        "import networkx as nx\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "import pkg_resources,imp\n",
        "imp.reload(pkg_resources)\n",
        "import spacy\n",
        "from spacy import displacy\n",
        "if language == 'en':\n",
        "  nlp = spacy.load('en_core_web_sm') \n",
        "else:\n",
        "  nlp = spacy.load('es_core_news_sm')\n",
        "nltk.download('punkt')\n",
        "%matplotlib inline\n",
        "# from textblob import TextBlob\n",
        "# TextBlob(\"bonjour esto es una 我 它\").detect_language()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQwcnzYmGq-p"
      },
      "source": [
        "# [BoundingBox](https://boundingbox.klokantech.com/) CSV RAW"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdhD7uzcGq-p"
      },
      "source": [
        "auth = tweepy.OAuthHandler(ckey, csecret)\n",
        "auth.set_access_token(atoken, asecret)\n",
        "api = tweepy.API(auth)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4YW2y9MKGq-4"
      },
      "source": [
        "class FiniteStreamListener(StreamListener):\n",
        "    \n",
        "    def __init__(self, number_of_tweets):\n",
        "        self.number_of_tweets = number_of_tweets\n",
        "        self.tweets = []\n",
        "        self.tweets_dict = []\n",
        "        pbar = pyprind.ProgBar(number_of_tweets)\n",
        "        super(FiniteStreamListener,self).__init__()\n",
        "        \n",
        "    def on_status(self, status):\n",
        "        if len(self.tweets) < self.number_of_tweets:\n",
        "            # if TextBlob(status.text).detect_language() == 'es':\n",
        "            self.tweets_dict.append(status._json)\n",
        "            place = status._json['place']['name'] if(status._json['place']) else \"\"\n",
        "            self.tweets.append({'date':status.created_at,\n",
        "                                'text':status.text,\n",
        "                                'location':place,\n",
        "                                'followers':status._json['user']['followers_count']})\n",
        "            pbar.update()\n",
        "        else:\n",
        "            return False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4Jo4Pw6Gq-7"
      },
      "source": [
        "finite_stream_listener = FiniteStreamListener(number_of_tweets=NUMBER_OF_TWEETS)\n",
        "streaming_api = Stream(auth=auth, listener=finite_stream_listener,timeout=5) #60"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVqBos_wGq-_"
      },
      "source": [
        "### The streaming API doesn't allow to filter by location AND keyword simultaneously."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QK5z4Ou3Gq-_"
      },
      "source": [
        "finite_stream_listener.tweets = []\n",
        "EMOTICONS = \">:] :-) :) :o) :] :3 :c) :> =] 8) =) :} :^) \"\n",
        "EMOTICONS = EMOTICONS.strip().split(' ')\n",
        "# streaming_api.filter(track=EMOTICONS,languages=[language],async=True)\n",
        "# streaming_api.filter(locations=GEO_LOCATION_BOUNDING_BOX,languages=[language],async=True)\n",
        "streaming_api.filter(locations=GEO_LOCATION_BOUNDING_BOX,track=EMOTICONS, languages=[language],async=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Kuo8f7eMycB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "93576c5d-e218-4cca-e3cc-71bed9c83060"
      },
      "source": [
        "print(len(finite_stream_listener.tweets))\n",
        "# 0% [##############################] 100% | ETA: 00:00:00\n",
        "# Total time elapsed: 01:05:42 \n",
        "random.sample([t['text']  for t in finite_stream_listener.tweets],2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['RT @ghwtss__: en fin, orgullosa de que esto pasó en Perú :) https://t.co/vSguM7Znti',\n",
              " 'RT @sheila_Corzo: Quiero amigos de twitter :) digan hola.\\U0001f97a']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVQU0KViGq_N"
      },
      "source": [
        "np.save('tweets_dict',finite_stream_listener.tweets_dict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkstNvrtGq_Q"
      },
      "source": [
        "def make_lowercase(tweet):\n",
        "    return tweet.lower()\n",
        "\n",
        "def remove_diacritics(tweet):\n",
        "    return unidecode(tweet)\n",
        "\n",
        "def remove_non_alpha_characters(tweet):\n",
        "    return ''.join(character for character in tweet if character.isalpha() or character == ' ')\n",
        "\n",
        "def remove_web_site(tweet):\n",
        "    return re.sub(r'http\\w+', '', tweet)#, flags=re.MULTILINE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-8gwW6SGq_T"
      },
      "source": [
        "tweets_df = pd.DataFrame.from_dict(finite_stream_listener.tweets)\n",
        "tweets_df.rename(columns={'text':'Tweets'},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcQmFTC7Gq_V"
      },
      "source": [
        "tweets_df['word_count'] = tweets_df['Tweets'].apply(lambda x: len(str(x).split(\" \")))\n",
        "tweets_df['char_count'] = tweets_df['Tweets'].str.len()\n",
        "\n",
        "def avg_word(sentence):\n",
        "    words = sentence.split()\n",
        "    return (sum(len(word) for word in words)/len(words))\n",
        "\n",
        "tweets_df['avg_word'] = tweets_df['Tweets'].apply(lambda x: avg_word(x))\n",
        "tweets_df['hastags'] = tweets_df['Tweets'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
        "tweets_df['numerics'] = tweets_df['Tweets'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZHeGLcgGq_a"
      },
      "source": [
        "cleaned_tweets = list(tweets_df['Tweets'])\n",
        "for cleaning_function in \\\n",
        "    [make_lowercase, \n",
        "     remove_diacritics,\n",
        "     remove_non_alpha_characters,\n",
        "     remove_web_site]:\n",
        "    cleaned_tweets = [cleaning_function(tweet) for tweet in cleaned_tweets]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRBvHlfyGq_d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "aa29d573-cb53-4341-fdd7-20d23807913d"
      },
      "source": [
        "random.sample(cleaned_tweets,3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['lovelymink hola remi nos ensenaras a cocinar ',\n",
              " 'el pedo de acomodar eso  es que esta termina siendo la cena  ',\n",
              " 'tejadaguadalup pa mi sos la o ']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 289
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbdB1ODOkuEO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e0ce4822-89b0-4fca-a9f5-58d5747125b0"
      },
      "source": [
        "KEYWORD = make_lowercase(remove_diacritics(remove_non_alpha_characters(remove_web_site(KEYWORD))))\n",
        "KEYWORD"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'podemos'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zisW-O4bGq_g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1cd26ed3-a5ce-4cbc-a87e-c8a13dfd1f11"
      },
      "source": [
        "number_of_occurences = sum('peru' in tweet for tweet in cleaned_tweets)\n",
        "print('Nuestra palabra clave aparece: {} veces'.format(number_of_occurences))\n",
        "print('Nuestra palabra clave apareció en: {}% de los tweets'.format(100 * number_of_occurences/NUMBER_OF_TWEETS))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nuestra palabra clave aparece: 5 veces\n",
            "Nuestra palabra clave apareció en: 2.5% de los tweets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qo2z0PJ0Gq_j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "144fca34-ca4c-4fac-b848-640dcc8566b2"
      },
      "source": [
        "pprint([tweet for tweet in cleaned_tweets if KEYWORD in tweet][:number_of_occurences//2+1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['rt romerotroll vamos amigoooos si podemos logra ese numero  canciones para '\n",
            " 'mi ex rt ',\n",
            " 'rt romerotroll vamos amigoooos si podemos logra ese numero  canciones para '\n",
            " 'mi ex rt ',\n",
            " 'rt romerotroll vamos amigoooos si podemos logra ese numero  canciones para '\n",
            " 'mi ex rt ']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5r_LrvUBGq_5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4ea63b9f-ef5d-466a-96f9-bd428927e569"
      },
      "source": [
        "indices_of_tweets_containing_keyword=[index for index, tweet in enumerate(cleaned_tweets) if KEYWORD in tweet]\n",
        "print('index de los 10 tweets:%s'%(indices_of_tweets_containing_keyword))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "index de los 10 tweets:[0, 2, 20, 22, 24, 30, 41, 65, 86, 116, 121, 130, 139, 148]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TZPwnPhGq__"
      },
      "source": [
        "distances_between_indices_of_tweets_containing_keyword = [\n",
        "    second_index - first_index for first_index, second_index in \\\n",
        "        zip(indices_of_tweets_containing_keyword[:-1], indices_of_tweets_containing_keyword[1:])\n",
        "]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYv77BARGrAG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "fb678f3b-9a12-447e-8ecf-506a6fc69714"
      },
      "source": [
        "pd.Series(distances_between_indices_of_tweets_containing_keyword).hist()\n",
        "plt.savefig('snapshot/lima_tweets_hist.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NE14naYGrAM"
      },
      "source": [
        "alpha = 1 + number_of_occurences\n",
        "beta = 1 + (NUMBER_OF_TWEETS - number_of_occurences)\n",
        "\n",
        "x_values = np.linspace(0, 1, 1002)[1:-1]\n",
        "pdf_y_values = beta_distribution(alpha, beta).pdf(x_values)\n",
        "cdf_y_values = np.cumsum(pdf_y_values) / np.sum(pdf_y_values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwon85MZGrAS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "1d2ce622-bad8-424d-e2a9-ef1aa3dd1fce"
      },
      "source": [
        "plt.figure(figsize=(18, 6))\n",
        "plt.subplot(121)\n",
        "plt.plot(x_values, pdf_y_values, label=(r'$\\alpha=%.1f,\\ \\beta=%.1f$' % (alpha, beta)))\n",
        "\n",
        "plt.xlim(0, 1)\n",
        "plt.xlabel('Probability of tweet containing keyword')\n",
        "plt.ylabel('Probability density')\n",
        "plt.title('Beta Distribution PDF')\n",
        "plt.legend(loc=1)\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(x_values, cdf_y_values)\n",
        "plt.xlim(0, 1)\n",
        "plt.ylim(0, 1.005)\n",
        "plt.yticks(np.linspace(0, 1, 21))\n",
        "plt.xlabel('Probability of tweet containing keyword')\n",
        "plt.ylabel('Cumulative probability')\n",
        "plt.title('Beta Distribution CDF')\n",
        "plt.savefig('snapshot/Beta Distribution CDF.png');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17S1ekByGrAX"
      },
      "source": [
        "ix = [n for n,b in enumerate((cdf_y_values>.5)&(cdf_y_values<.95)) if b]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TrWznl_GrAb"
      },
      "source": [
        "range_ =cdf_y_values[ix]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjTYPbyoGrAh"
      },
      "source": [
        "a=np.c_[x_values[ix],cdf_y_values[ix]]\n",
        "max_ix,_ = np.unravel_index(a.argmax(), a.shape)\n",
        "min_ix,_ = np.unravel_index(a.argmin(), a.shape)\n",
        "fifth_percentile, ninety_fifth_percentile = x_values[max_ix],x_values[min_ix]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXPKRlrgGrAr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ad9a4588-fc26-4487-8b2c-00822faf73e4"
      },
      "source": [
        "print('Con 90% de certeza digamos que la verdadera probabilidad se encuentra entre: {} y {}'.format(\n",
        "    round(fifth_percentile, 10), round(ninety_fifth_percentile, 10)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Con 90% de certeza digamos que la verdadera probabilidad se encuentra entre: 0.023976024 y 0.000999001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCK4CLU9GrAw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "18877da0-99a0-40f5-dcde-e55b29c62e07"
      },
      "source": [
        "def compute_total_probability_that_probability_less_than_p(p):\n",
        "    return max(cumulative_prob for cumulative_prob, x_value in zip(cdf_y_values, x_values) if x_value < p)\n",
        "\n",
        "print('La probabilidad que la verdadera probabilidad es > .1 es: {}'.format(\n",
        "    1 - compute_total_probability_that_probability_less_than_p(.1)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "La probabilidad que la verdadera probabilidad es > .1 es: 3.328883511244385e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQwp8QP1pPpb"
      },
      "source": [
        "tweets_has_location_df = tweets_df[tweets_df['location']!='']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQ9ECRsdGrA1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "97bce4f1-8b45-4b42-c3a4-ac39c8416293"
      },
      "source": [
        "tweets_has_location_df.value_counts(subset=['location'],ascending=True).plot(kind='barh')\n",
        "plt.title('Most Frequent locations')\n",
        "plt.xlabel('Cantidad')\n",
        "plt.tight_layout()\n",
        "plt.savefig('snapshot/most Frequent locations.png');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7uftv9GkGrBK"
      },
      "source": [
        "tweets_df.to_csv('lima_tweets.csv',index=None)\n",
        "tweets_has_location_df.to_csv('lima_tweets_location.csv',index=None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFeMp4-OGrBM"
      },
      "source": [
        "tweets_df = pd.read_csv('lima_tweets.csv')\n",
        "tweets_has_location_df = pd.read_csv('lima_tweets_location.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwmkja1aGrBP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "02bf116c-f439-4ab3-e570-82caf367109d"
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "stop = stopwords.words('spanish') "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JedRxzniGrBS"
      },
      "source": [
        "doc_complete = tweets_df.Tweets.values\n",
        "exclude = set(string.punctuation) \n",
        "lemma = WordNetLemmatizer()\n",
        "def clean(doc):\n",
        "    stop_free = \" \".join([i for i in doc.lower().split() if i not in stop])\n",
        "    punc_free = ''.join(ch for ch in stop_free if ch not in exclude)\n",
        "    normalized = \" \".join(lemma.lemmatize(word) for word in punc_free.split())\n",
        "    return normalized\n",
        "\n",
        "doc_clean = [clean(doc).split() for doc in doc_complete]   \n",
        "tweets_df.dropna(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtatm1ZEGrBU"
      },
      "source": [
        "tweets_df['Tweets_clean'] = pd.Series(doc_clean).apply(lambda x:' '.join(x))\n",
        "tweets_df['word_count'] = tweets_df['Tweets_clean'].apply(lambda x: len(str(x).split(\" \")))\n",
        "tweets_df['char_count'] = tweets_df['Tweets_clean'].str.len()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXsnnCa6GrBW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "92409e67-eb49-4038-e5fd-dea5d1d8406f"
      },
      "source": [
        "def avg_word(sentence):\n",
        "    words = sentence.split()\n",
        "    if len(words)==0:\n",
        "        return 0\n",
        "    return (sum(len(word) for word in words)/len(words))\n",
        "\n",
        "tweets_df['avg_word'] = tweets_df['Tweets_clean'].apply(lambda x: avg_word(x))\n",
        "tweets_df['hastags'] = tweets_df['Tweets'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
        "tweets_df['numerics'] = tweets_df['Tweets'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))\n",
        "tweets_df.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>location</th>\n",
              "      <th>followers</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>Tweets_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-10-09 04:11:20</td>\n",
              "      <td>Poco a poco todo se va acomodando</td>\n",
              "      <td>Santa Anita</td>\n",
              "      <td>170</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>va acomodando</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-10-09 04:11:25</td>\n",
              "      <td>Hay gente que han perdido la vergüenza, preten...</td>\n",
              "      <td>Jesús Maria</td>\n",
              "      <td>51</td>\n",
              "      <td>7</td>\n",
              "      <td>51</td>\n",
              "      <td>6.428571</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>gente perdido vergüenza pretender ser candidat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-10-09 04:11:25</td>\n",
              "      <td>Éste Viernes de Sankirtana, nos visita Hanumat...</td>\n",
              "      <td>Lurigancho</td>\n",
              "      <td>401</td>\n",
              "      <td>14</td>\n",
              "      <td>116</td>\n",
              "      <td>7.357143</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>éste viernes sankirtana visita hanumat prana d...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   date  ...                                       Tweets_clean\n",
              "4   2020-10-09 04:11:20  ...                                      va acomodando\n",
              "11  2020-10-09 04:11:25  ...  gente perdido vergüenza pretender ser candidat...\n",
              "12  2020-10-09 04:11:25  ...  éste viernes sankirtana visita hanumat prana d...\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JbKK0bseGrBa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "c31bee2b-84fd-46ad-869b-abc785a6d0a3"
      },
      "source": [
        "freq = pd.Series(' '.join(tweets_df['Tweets_clean']).split()).value_counts()[:5]\n",
        "freq.plot(kind='barh')\n",
        "plt.title('Most Frequent words')\n",
        "plt.xlabel('Count')\n",
        "plt.tight_layout()\n",
        "plt.savefig('snapshot/most Frequent words.png');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGq7C0LVGrBd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "54d2b5cf-e01d-43b3-c7f3-babbd193edd5"
      },
      "source": [
        "pprint(tweets_df['Tweets_clean'][:2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4                                         va acomodando\n",
            "11    gente perdido vergüenza pretender ser candidat...\n",
            "Name: Tweets_clean, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fr2Zd6ReGrBf"
      },
      "source": [
        "dictionary = corpora.Dictionary(tweets_df['Tweets_clean'].apply(lambda x:x.split()))\n",
        "# Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.\n",
        "doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cwvv6PmRGrBi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "91f75786-bc53-461c-f57b-becad4394419"
      },
      "source": [
        "pprint(doc_term_matrix[:2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[], [(86, 1)]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoxzAYYmGrBk"
      },
      "source": [
        "Lda = gensim.models.ldamodel.LdaModel\n",
        "ldamodel = Lda(doc_term_matrix, num_topics=3, id2word = dictionary, passes=50)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yK9YfL5XGrBn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "8767584f-98a2-40fb-b180-fd5b041097f0"
      },
      "source": [
        "from pprint import pprint\n",
        "pprint(ldamodel.print_topics(num_topics=3, num_words=3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(0, '0.056*\"va\" + 0.056*\"da\" + 0.035*\"ser\"'),\n",
            " (1, '0.061*\"gusta\" + 0.028*\"haciendo\" + 0.028*\"hizo\"'),\n",
            " (2, '0.077*\"buenas\" + 0.056*\"noches\" + 0.056*\"amigo\"')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpkZb_smGrBq"
      },
      "source": [
        "# from gensim.test.utils import datapath\n",
        "# fname = datapath(\"lda_lima_tweet_model\")\n",
        "ldamodel.save(\"lda_lima_tweet_model\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "we2vTlmGGrBx"
      },
      "source": [
        "from gensim.models.ldamodel import LdaModel\n",
        "ldamodel = LdaModel.load(\"lda_lima_tweet_model\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfMGAm44GrB0"
      },
      "source": [
        "doc_lda = ldamodel[doc_term_matrix]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38jMYB9jGrB3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "133b0f1c-333e-408a-dab0-4bca0108093a"
      },
      "source": [
        "print('Perplexity: ', ldamodel.log_perplexity(doc_term_matrix))  # a measure of how good the model is. lower the better"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Perplexity:  -5.379126834037385\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwVZoVuoGrB7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "f8398a5a-203c-40e5-b24b-e137cfe4414a"
      },
      "source": [
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim.prepare(ldamodel, doc_term_matrix, dictionary)\n",
        "vis"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "\n",
              "<div id=\"ldavis_el971396375021248246791669563\"></div>\n",
              "<script type=\"text/javascript\">\n",
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              "        new LDAvis(\"#\" + \"ldavis_el971396375021248246791669563\", ldavis_el971396375021248246791669563_data);\n",
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              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el971396375021248246791669563\", ldavis_el971396375021248246791669563_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ],
            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "1      0.100282 -0.075889       1        1  41.999370\n",
              "2     -0.124327 -0.038993       2        1  29.245487\n",
              "0      0.024046  0.114882       3        1  28.755144, topic_info=       Term      Freq     Total Category  logprob  loglift\n",
              "119  buenas  4.000000  4.000000  Default  30.0000  30.0000\n",
              "122  noches  3.000000  3.000000  Default  29.0000  29.0000\n",
              "1        va  3.000000  3.000000  Default  28.0000  28.0000\n",
              "54    amigo  3.000000  3.000000  Default  27.0000  27.0000\n",
              "86    gusta  5.000000  5.000000  Default  26.0000  26.0000\n",
              "..      ...       ...       ...      ...      ...      ...\n",
              "42      dio  0.732745  1.134113   Topic3  -4.2741   0.8095\n",
              "41   cólera  0.732745  1.134113   Topic3  -4.2741   0.8095\n",
              "31   muchas  1.802959  2.873987   Topic3  -3.3737   0.7801\n",
              "3     gente  0.764516  2.263886   Topic3  -4.2316   0.1608\n",
              "43     hizo  0.735490  3.044914   Topic3  -4.2704  -0.1743\n",
              "\n",
              "[127 rows x 6 columns], token_table=      Topic      Freq        Term\n",
              "term                             \n",
              "77        3  0.881755      alexis\n",
              "54        2  0.879114       amigo\n",
              "23        1  0.818875        anja\n",
              "78        3  0.881755        aria\n",
              "55        2  0.870030  aziraphale\n",
              "...     ...       ...         ...\n",
              "123       2  0.870027           🌛\n",
              "124       2  0.870027         👏👏👏\n",
              "48        2  0.870018           😍\n",
              "125       2  0.870027           🙏\n",
              "76        2  0.870024     🤦🏻‍♂️xd\n",
              "\n",
              "[97 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 3, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CZ_1VveGrB-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f1def4b8-f9bd-49d1-8410-93388f57f2f4"
      },
      "source": [
        "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
        "    texts_out = []\n",
        "    for sent in texts:\n",
        "        doc_ = nlp(sent)\n",
        "        texts_out.append(list(set([str(c.head) for c in doc_ if c.head.tag_.startswith(tuple(allowed_postags))])))\n",
        "    return texts_out\n",
        "\n",
        "lemmatization(tweets_df['Tweets_clean'][:5],['VERB'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['acomodando'], ['pretender'], ['da'], [], ['cuídate']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUKd4S9VGrCA"
      },
      "source": [
        "def join_comma(row_list):\n",
        "    if row_list == []:\n",
        "        return np.NaN\n",
        "    else:\n",
        "        return ', '.join(row_list)\n",
        "\n",
        "tweets_df['ACTIONS']=pd.Series(lemmatization(tweets_df['Tweets_clean'],['VERB'])).apply(join_comma)\n",
        "tweets_df['NOUNS']=pd.Series(lemmatization(tweets_df['Tweets_clean'],['NOUN'])).apply(join_comma)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctE8O8UkGrCD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "82fac4e9-c861-48a9-abff-9c28bf801273"
      },
      "source": [
        "tweets_df[['Tweets_clean','NOUNS','ACTIONS']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tweets_clean</th>\n",
              "      <th>NOUNS</th>\n",
              "      <th>ACTIONS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>va acomodando</td>\n",
              "      <td>desgracia</td>\n",
              "      <td>cuídate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>gente perdido vergüenza pretender ser candidat...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>éste viernes sankirtana visita hanumat prana d...</td>\n",
              "      <td>piqueos, basura</td>\n",
              "      <td>comer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>untkcqr emilia14614784 luvwaifus cuídate anja ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hagan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>ohohuh1 cuídate mucho desgracia todo</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Tweets_clean            NOUNS  ACTIONS\n",
              "4                                       va acomodando        desgracia  cuídate\n",
              "11  gente perdido vergüenza pretender ser candidat...              NaN      NaN\n",
              "12  éste viernes sankirtana visita hanumat prana d...  piqueos, basura    comer\n",
              "14  untkcqr emilia14614784 luvwaifus cuídate anja ...              NaN    hagan\n",
              "33               ohohuh1 cuídate mucho desgracia todo              NaN      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOUx5UBjGrCF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "7f711ad6-2cb6-48b6-ef85-4793b7796bfa"
      },
      "source": [
        "tweets_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>location</th>\n",
              "      <th>followers</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>Tweets_clean</th>\n",
              "      <th>ACTIONS</th>\n",
              "      <th>NOUNS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-10-09 04:11:20</td>\n",
              "      <td>Poco a poco todo se va acomodando</td>\n",
              "      <td>Santa Anita</td>\n",
              "      <td>170</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>va acomodando</td>\n",
              "      <td>cuídate</td>\n",
              "      <td>desgracia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-10-09 04:11:25</td>\n",
              "      <td>Hay gente que han perdido la vergüenza, preten...</td>\n",
              "      <td>Jesús Maria</td>\n",
              "      <td>51</td>\n",
              "      <td>7</td>\n",
              "      <td>51</td>\n",
              "      <td>6.428571</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>gente perdido vergüenza pretender ser candidat...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-10-09 04:11:25</td>\n",
              "      <td>Éste Viernes de Sankirtana, nos visita Hanumat...</td>\n",
              "      <td>Lurigancho</td>\n",
              "      <td>401</td>\n",
              "      <td>14</td>\n",
              "      <td>116</td>\n",
              "      <td>7.357143</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>éste viernes sankirtana visita hanumat prana d...</td>\n",
              "      <td>comer</td>\n",
              "      <td>piqueos, basura</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-10-09 04:11:27</td>\n",
              "      <td>@UnTkCqr @Emilia14614784 @LuvWaifus Cuídate mu...</td>\n",
              "      <td>Villa el Salvador</td>\n",
              "      <td>371</td>\n",
              "      <td>13</td>\n",
              "      <td>107</td>\n",
              "      <td>7.307692</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>untkcqr emilia14614784 luvwaifus cuídate anja ...</td>\n",
              "      <td>hagan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2020-10-09 04:11:43</td>\n",
              "      <td>@ohohuh1 :( cuídate mucho.\\n\\nQué desgracia, t...</td>\n",
              "      <td>Magdalena del Mar</td>\n",
              "      <td>196</td>\n",
              "      <td>5</td>\n",
              "      <td>36</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>ohohuh1 cuídate mucho desgracia todo</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   date  ...            NOUNS\n",
              "4   2020-10-09 04:11:20  ...        desgracia\n",
              "11  2020-10-09 04:11:25  ...              NaN\n",
              "12  2020-10-09 04:11:25  ...  piqueos, basura\n",
              "14  2020-10-09 04:11:27  ...              NaN\n",
              "33  2020-10-09 04:11:43  ...              NaN\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlBB6cxMGrCH"
      },
      "source": [
        "tweets_df.to_csv('tweets_solutions.csv',index=None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrTbtR9EGrCL"
      },
      "source": [
        "tweets_df = pd.read_csv('tweets_solutions.csv')\n",
        "tweets_df.dropna(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5_y9mqtGrCP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "ea701c0c-977c-435a-c860-92c9eb376909"
      },
      "source": [
        "tweets_df['Tweets_clean']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                                        va acomodando\n",
              "2    éste viernes sankirtana visita hanumat prana d...\n",
              "Name: Tweets_clean, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXm8kPtqGrCT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "fa4e715b-1b75-410b-fb57-3e5c48dafbde"
      },
      "source": [
        "tweets_df['Tweets_clean'] = tweets_df['Tweets_clean'].apply(lambda x: \" \".join([Word(word).lemmatize() for word in x.split()]))\n",
        "print(tweets_df.shape)\n",
        "tweets_df['Tweets_clean'].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2, 12)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                                        va acomodando\n",
              "2    éste viernes sankirtana visita hanumat prana d...\n",
              "Name: Tweets_clean, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsJLbnTeGrCW"
      },
      "source": [
        "# displacy.serve(doc, style=\"dep\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOSJ2BGSGrCd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "4d49e8f1-2c95-4f97-9698-dd90d0f1c1b5"
      },
      "source": [
        "doc = nlp(tweets_df['Tweets_clean'].iloc[-1])\n",
        "from IPython.display import HTML, Image, display\n",
        "displacy.render(doc, style=\"dep\",jupyter=True,options={'distance':100})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"es\" id=\"3ce51d42bea54cf39272eb89a029b8e7-0\" class=\"displacy\" width=\"1450\" height=\"337.0\" direction=\"ltr\" style=\"max-width: none; height: 337.0px; color: #000000; background: #ffffff; font-family: Arial; direction: ltr\">\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">éste</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">PRON</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"150\">viernes</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"150\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"250\">sankirtana</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"250\">ADJ</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"350\">visita</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"350\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"450\">hanumat</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"450\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"550\">prana</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"550\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"650\">da</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"650\">VERB</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"750\">gran</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"750\">ADJ</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"850\">entusiasta</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"850\">ADJ</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"950\">sankirtanero</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"950\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1050\">perú</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1050\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1150\">méxico</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1150\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1250\">colombi…</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1250\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"247.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1350\">httpstco4hqv9gupmk</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1350\">PROPN</tspan>\n",
              "</text>\n",
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              "<IPython.core.display.HTML object>"
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          },
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BB6t0TIcGrCg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "010a3c75-05c6-45a7-b07e-8a72608bb5b6"
      },
      "source": [
        "displacy.render(doc, style=\"ent\",jupyter=True,)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">éste viernes sankirtana visita hanumat prana da gran entusiasta sankirtanero perú méxico colombi\n",
              "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    … httpstco4hqv9gupmk\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">MISC</span>\n",
              "</mark>\n",
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          "metadata": {
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsjGrCmMGrCi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "c05b27ad-32e6-4f18-95b1-cf3da79665d6"
      },
      "source": [
        "TextBlob(tweets_df['Tweets_clean'].iloc[-1]).ngrams(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[WordList(['éste', 'viernes']),\n",
              " WordList(['viernes', 'sankirtana']),\n",
              " WordList(['sankirtana', 'visita']),\n",
              " WordList(['visita', 'hanumat']),\n",
              " WordList(['hanumat', 'prana']),\n",
              " WordList(['prana', 'da']),\n",
              " WordList(['da', 'gran']),\n",
              " WordList(['gran', 'entusiasta']),\n",
              " WordList(['entusiasta', 'sankirtanero']),\n",
              " WordList(['sankirtanero', 'perú']),\n",
              " WordList(['perú', 'méxico']),\n",
              " WordList(['méxico', 'colombi…']),\n",
              " WordList(['colombi…', 'httpstco4hqv9gupmk'])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7RrEC-IGrCk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "fcfe9870-2515-4f57-d00b-755f68033409"
      },
      "source": [
        "tf1 = (tweets_df['Tweets_clean']).apply(lambda x: pd.value_counts(x.split(\" \"))).sum(axis = 0).reset_index()\n",
        "tf1.columns = ['words','tf']\n",
        "for i,word in enumerate(tf1['words']):\n",
        "    tf1.loc[i, 'idf'] = np.log(tweets_df.shape[0]/(len(tweets_df[tweets_df['Tweets_clean'].str.contains(word)])))\n",
        "\n",
        "tf1['tfidf'] = tf1['tf'] * tf1['idf']\n",
        "print(tf1.shape)\n",
        "tf1.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(16, 4)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>words</th>\n",
              "      <th>tf</th>\n",
              "      <th>idf</th>\n",
              "      <th>tfidf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>acomodando</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>va</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>perú</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>gran</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>sankirtanero</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>da</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>méxico</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>hanumat</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>éste</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>colombi…</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          words   tf       idf     tfidf\n",
              "0    acomodando  1.0  0.693147  0.693147\n",
              "1            va  1.0  0.693147  0.693147\n",
              "2          perú  1.0  0.693147  0.693147\n",
              "3          gran  1.0  0.693147  0.693147\n",
              "4  sankirtanero  1.0  0.693147  0.693147\n",
              "5            da  1.0  0.000000  0.000000\n",
              "6        méxico  1.0  0.693147  0.693147\n",
              "7       hanumat  1.0  0.693147  0.693147\n",
              "8          éste  1.0  0.693147  0.693147\n",
              "9      colombi…  1.0  0.693147  0.693147"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "104JtA-sGrCn"
      },
      "source": [
        "stop = set(stopwords.words('spanish'))\n",
        "stop |= set(['lima','si','ser'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOXx4IfXGrCo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "2224b188-a14e-4713-eae9-06eb25599032"
      },
      "source": [
        "tfidf = TfidfVectorizer(max_features=1000, lowercase=True, analyzer='word', stop_words= stop,ngram_range=(1,1))\n",
        "train_vect = tfidf.fit_transform(tweets_df['Tweets_clean'])\n",
        "train_vect"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<2x16 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 16 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRUM6oA4GrCt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "38b634c0-f8f0-41a3-90e0-d13fee1fd4dd"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "bow = CountVectorizer(max_features=1000, lowercase=True, ngram_range=(1,1),analyzer = \"word\")\n",
        "train_bow = bow.fit_transform(tweets_df['Tweets_clean'])\n",
        "print(train_bow.shape)\n",
        "train_bow"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2, 16)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<2x16 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 16 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P236la94GrCv"
      },
      "source": [
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "\n",
        "def find_similar(tfidf_matrix, index, top_n = 5):\n",
        "    cosine_similarities = linear_kernel(tfidf_matrix[index:index+1], tfidf_matrix).flatten()\n",
        "    related_docs_indices = [i for i in cosine_similarities.argsort()[::-1] if i != index]\n",
        "    return [(index, cosine_similarities[index]) for index in related_docs_indices][0:top_n]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaSpWD5oGrCy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "outputId": "f005cd42-0fe5-442b-afec-d7f6fe6db560"
      },
      "source": [
        "tweet = tweets_df.sample(1)\n",
        "tweet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>location</th>\n",
              "      <th>followers</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>Tweets_clean</th>\n",
              "      <th>ACTIONS</th>\n",
              "      <th>NOUNS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-10-09 04:11:25</td>\n",
              "      <td>Éste Viernes de Sankirtana, nos visita Hanumat...</td>\n",
              "      <td>Lurigancho</td>\n",
              "      <td>401</td>\n",
              "      <td>14</td>\n",
              "      <td>116</td>\n",
              "      <td>7.357143</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>éste viernes sankirtana visita hanumat prana d...</td>\n",
              "      <td>comer</td>\n",
              "      <td>piqueos, basura</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  date  ...            NOUNS\n",
              "2  2020-10-09 04:11:25  ...  piqueos, basura\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0xeCmaEGrC1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "39a9dea1-a252-449a-f316-8b670ba86b05"
      },
      "source": [
        "print(tweet['Tweets'].values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Éste Viernes de Sankirtana, nos visita Hanumat Prana Das, un gran y entusiasta sankirtanero. \\nPerú /México /Colombi… https://t.co/4hqv9gupmK']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbwXoco7GrC3"
      },
      "source": [
        "tweet.reset_index(drop=True,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5reTsQtQGrC6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "1f03f6a6-3d32-4e87-8173-2c2d03990476"
      },
      "source": [
        "pd.options.display.max_colwidth = 120\n",
        "vals = pd.DataFrame()\n",
        "for index, score in find_similar(train_vect, tweet.index[0],top_n = 5):\n",
        "    vals = vals.append(tweets_df.iloc[index:index+1,:])\n",
        "    vals.loc[index,'score'] = score\n",
        "    \n",
        "vals[['Tweets','score']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tweets</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Éste Viernes de Sankirtana, nos visita Hanumat Prana Das, un gran y entusiasta sankirtanero. \\nPerú /México /Colombi...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                    Tweets  score\n",
              "2  Éste Viernes de Sankirtana, nos visita Hanumat Prana Das, un gran y entusiasta sankirtanero. \\nPerú /México /Colombi...    NaN\n",
              "1                                                                                                                      NaN    0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2F9cn2eGrC-"
      },
      "source": [
        "corpus = nlp('\\n'.join(tweets_df['NOUNS'].dropna()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcV2upsFGrDD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "808101e6-e786-4f86-e24a-66a786d78350"
      },
      "source": [
        "visited = {}\n",
        "nouns = []\n",
        "for word in corpus:\n",
        "    if word.pos_.startswith('N') and len(word.string) < 15 and len(word.string) > 2:\n",
        "        token = word.string.strip().lower()\n",
        "        if token in visited:\n",
        "            visited[token] += 1\n",
        "            continue\n",
        "        else:\n",
        "            visited[token] = 1\n",
        "            nouns.append(word)\n",
        "nouns = sorted(nouns, key=lambda w: -visited[w.string.strip().lower()])[:150]\n",
        "pd.DataFrame([[w.text, visited[w.string.strip().lower()]] for w in nouns], columns=['Noun', 'Freq'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Noun</th>\n",
              "      <th>Freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>desgracia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>piqueos</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>basura</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Noun  Freq\n",
              "0  desgracia     1\n",
              "1    piqueos     1\n",
              "2     basura     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IG1DDB3XGrDG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "a05546d1-c8eb-4ba5-d184-fce555496cde"
      },
      "source": [
        "def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):\n",
        "    assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n",
        "    plt.figure() # in inches\n",
        "    for i, label in enumerate(labels):\n",
        "        x, y = low_dim_embs[i, :]\n",
        "        plt.scatter(x, y, s=2.0)\n",
        "        plt.annotate(label, xy=(x, y), xytext=(5, 2),\n",
        "                 textcoords='offset points',ha='right',va='bottom')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('snapshot/lima_words_TSNE.png')\n",
        "    plt.show()\n",
        "\n",
        "# Creating the tsne plot [Warning: will take time]\n",
        "tsne = TSNE(perplexity=50.0, n_components=2, init='pca', n_iter=10000)\n",
        "\n",
        "low_dim_embedding = tsne.fit_transform(np.array([word.vector for word in nouns]))\n",
        "\n",
        "# Finally plotting and saving the fig \n",
        "plot_with_labels(low_dim_embedding, [word.text for word in nouns])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvmqBsLMGrDJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        },
        "outputId": "bb9681d9-8ab6-4779-8065-fd41f14051cf"
      },
      "source": [
        "tweets_df = pd.read_csv('tweets_solutions.csv')\n",
        "tweets_df.head(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>Tweets</th>\n",
              "      <th>location</th>\n",
              "      <th>followers</th>\n",
              "      <th>word_count</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>Tweets_clean</th>\n",
              "      <th>ACTIONS</th>\n",
              "      <th>NOUNS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-10-09 04:11:20</td>\n",
              "      <td>Poco a poco todo se va acomodando</td>\n",
              "      <td>Santa Anita</td>\n",
              "      <td>170</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>va acomodando</td>\n",
              "      <td>cuídate</td>\n",
              "      <td>desgracia</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  date                             Tweets  ...  ACTIONS      NOUNS\n",
              "0  2020-10-09 04:11:20  Poco a poco todo se va acomodando  ...  cuídate  desgracia\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsjXRKTGGrDP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a842c15b-3558-4ed7-c451-a73f9e3eca4d"
      },
      "source": [
        "hashtag_summary = adv.extract_hashtags(tweets_df['Tweets'])\n",
        "hashtag_summary.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['hashtags', 'hashtags_flat', 'hashtag_counts', 'hashtag_freq', 'top_hashtags', 'overview'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDnYMAfZGrDR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "8715a1a1-29d1-4810-bc50-b9b71d0ba0ff"
      },
      "source": [
        "hashtag_summary['overview']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'hashtags_per_post': 0.0,\n",
              " 'num_hashtags': 0,\n",
              " 'num_posts': 20,\n",
              " 'unique_hashtags': 0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gU45Lg8tGrDX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c94f7da2-282a-426f-eaca-86dc1da07dc5"
      },
      "source": [
        "hashtag_summary['hashtags'][:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[], [], [], [], []]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jt6G_xdIGrDa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f50f66f4-ac95-4dac-96c7-fe52737635d9"
      },
      "source": [
        "hashtag_summary['hashtag_counts'][:20]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IfQjn5SGrDc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f9b34c34-60f6-4019-8a31-567af409b67c"
      },
      "source": [
        "hashtag_summary['hashtag_freq'][:20]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 20)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0xHt2ZTTGrDf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "a4503634-52f5-4c8b-def6-0c4f977c33ae"
      },
      "source": [
        "plt.figure(facecolor='#ebebeb', figsize=(11, 8))\n",
        "plt.bar([x[0] for x in hashtag_summary['hashtag_freq'][:15]],\n",
        "        [x[1] for x in hashtag_summary['hashtag_freq'][:15]])\n",
        "plt.title('Hashtag frequency')\n",
        "plt.xlabel('Hashtags per tweet')\n",
        "plt.ylabel('Number of tweets')\n",
        "plt.yscale('log')\n",
        "plt.grid(alpha=0.5)\n",
        "plt.gca().set_frame_on(False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjPHJ2HQ4HBo5cqQcDoe3RwEAAMBN+Ht7gN/DbrfLbrd7ewwAAAB44JNnNgEAAOAbiE0AAAAYQ2wCAADAGGITAAAAxhCbAAAAMIbYBAAAgDHEJgAAAIwhNgEAAGAMsQkAAABjiE0AAAAYQ2wCAADAGGITAAAAxhCbAAAAMIbYBAAAgDHEJgAAAIwhNgEAAGAMsQkAAABjiE0AAAAYQ2wCAADAGGITAAAAxhCbAAAAMIbYBAAAgDHEJgAAAIwhNgEAAGAMsQkAAABjiE0AAAAYQ2wCAADAGGITAAAAxhCbAAAAMIbYBAAAgDHEJgAAAIwhNgEAAGAMsQkAAABjPMbmihUrlJOTI0maNm2aHnnkEe3Zs8f4YAAAAPB9HmPzzTffVFBQkLZv364tW7aof//+Gjt2rBWzAQAAwMd5jE0/v+t3Wb9+veLi4tSxY0fl5uYaHwwAAAC+z2NsVqhQQaNGjdKKFSvUvn17Xb58WU6n04rZCuRwODRy5Eg5HA6vzgEAAICb8/d0h7///e/auHGjhg0bpr/85S/KysrSSy+9ZMVsBbLb7bLb7V6dAQAAAJ55PLM5evRode3aVaGhoZKk4OBgLV261PhgAAAA8H0eY3Pv3r35Lufl5fFudAAAABRKgU+jv/XWW3rrrbf0yy+/qFq1aq7XaQYEBGjQoEGWDQgAAADfVWBsjhgxQiNGjNCkSZP04osvWjkTAAAAigiPT6O/8MILWrp0qaZNmyZJyszMVFpamvHBAAAA4Ps8xmZCQoK++uorLVu2TJJUsmRJJSQkGB8MAAAAvs9jbO7atUtTpkxR8eLFJUmlS5fWlStXjA8GAAAA3+cxNosVK6a8vDzZbDZJ0qlTp1xfAwAAADfjMTafeOIJPfzww8rOztbf/vY3de3aVSNGjLBiNgAAAPg4j58gFBsbq4YNG2rLli1yOp1KTExUzZo1rZgNAAAAPs7jmU1J+ve//60777xTjz/+uO6++26lp6ebngsAAABFgMfYfOONNzRr1izNmDFDknT16lUNHTrU+GAAAADwfR5jc/Xq1UpKSlKJEiUkXf9s9AsXLhgfDAAAAL7PY2wGBATIZrO53oFOaAIAAKCwPMZmjx49NGrUKJ07d04LFy5UbGysBg4caMVsAAAA8HEe343+5JNPatOmTbrrrrt04MABJSQkKDo62oLRAAAA4Os8xmZSUpLCw8M1YcIEC8YBAABAUeIxNjMzMzV69GhlZGSoQYMGCg8PV1hYmOrXr2/FfAAAAPBhHmMzISFBknTp0iUtXLhQc+bM0bhx43TixAnjwwEAAMC3eYzNadOmaefOnbpw4YLq16+vCRMmKCwszIrZAAAA4OM8xubq1avl7++vDh06KCIiQg888ICKFy9uxWwAAADwcR5/9NHGjRu1bNkyNW7cWJs2bVKrVq3UpUsXK2YDAACAj/N4ZvPHH3/U9u3btXXrVu3evVuVKlXiaXQAAAAUisfYnDRpksLDw/XEE0+ocePGKlasmBVzAQAAoAjw+DR6q1at9PTTT6t58+au0HznnXeMDwYAAADf5zE2P/rooxuuS05ONjIMAAAAipYCn0b/5JNPtGzZMh05ciTfZ6Hn5OSoTJkylgwHAAAA31ZgbDZr1kzly5fX6dOnNXToUNf1QUFBqlu3riXDAQAAwLcVGJuVK1dW5cqVtWbNGivnAQAAQBHi8TWbAAAAwO9FbAIAAMCYAmOzV69ekqSJEydaNgwAAACKlgJfs3nixAnt3LlTa9euVUxMjJxOZ77bGzZsaHw4AAAA+LYCYzMhIUHTpk3TsWPHNH78+Hy32Ww2paSkGB8OAAAAvq3A2Ozevbu6d++uadOmadSoUVbOBAAAgCLC42ejjxo1SmvXrtW2bdskSZGRkerYsaPxwQAAAOD7PL4bfdKkSZo/f75q1qypmjVrav78+XrllVesmA0AAAA+zuOZzc8++0yff/65/Pyud2m/fv3Utm1bjRs3zvhwAAAA8G2F+jmbZ8+edX197tw5Y8MAAACgaPF4ZvOZZ55R27ZtFRkZKafTqe3bt3NWEwAAAIXiMTZ79eqliIgI7d69W5I0fvx4lS9f3vhgAAAA8H0eY1OSgoOD1alTJ9OzAAAAoIjhs9EBAABgDLEJAAAAY24am3l5eQoPD7dqFgAAABQxN43NO+64QzVq1NDRo0etmgcAAABFiMc3CJ05c0ZRUVFq0qSJSpQo4bo+KSnJ6GAAAADwfR5j89lnn7VijlvicDjkcDhkt9tlt9u9PQ4AAAAK4DE2IyMjlZGRoUOHDql169a6ePGirl27ZsVsBSIyAQAAfIPHd6MvXLhQjz32mEaPHi1JOn78uOLi4owPBgAAAN/nMTbfe+89rV69WkFBQZKk0NBQnTp1yvhgAAAA8H0eY7N48eIKCAhwXb569apsNpvRoQAAAFA0eHzNZkREhKZPn65ffvlFmzZt0gcffKCOHTtaMRsAAAB8nMczmy+++KLKli2r2rVr6x//+Ifat2+v559/3orZAAAA4OM8ntn08/NT37591aRJE9lsNtWoUYOn0QEAAFAoHmNz3bp1GjNmjEJCQuR0OnXkyBFNnTpV7du3t2I+AAAA+DCPsfnSSy8pJSVF1atXlyQdPnxY/fv3JzYBAADgkcfXbAYFBblCU5JCQkJcPwYJAAAAuJkCz2z+85//lCQ1atRI/fr1U48ePWSz2bRy5Uo1btzYsgEBAADguwqMTYfD4fq6XLly2rp1qyTpnnvu0aVLl8xPBgAAAJ9XYGzOmjXLyjkAAABQBHl8g1B6err+/ve/KyMjQ1evXnVdn5SUZHQwAAAA+D6Psfnwww+rf//+stvt/HxNAAAA3BKPsVm8eHHFx8dbMQsAAACKGI+xGR8frzfeeEPR0dEKCAhwXd+wYUOjgwEAAMD3eYzNH374QUuXLtUXX3whP7/rP5bTZrMpJSXF+HAAAADwbR5jc+XKlfr666/zndUEAAAACsPjJwjVrl1bZ8+etWIWAAAAFDEez2yePXtWERERatSokYoXL+66nh99BAAAAE88xubYsWOtmAMAAABFkMfYjIyMtGIOAAAAFEEeYzMkJMT1w9yvXLmiK1euqESJEjp8+LDx4QAAAODbPMbmv/71L9fXTqdTa9as0ddff21yJgAAABQRHt+N/ls2m00PPvigPv/8c1PzAAAAoAjxeGbzn//8p+vra9euaffu3QoMDDQ6FAAAAIoGj7HpcDj+/539/VW5cmUlJiYaHQoAAABFg8fYnDVrlhVzAAAAoAgqMDanTp1a4INsNptGjRplZCAAAAAUHQXGZokSJW647uLFi1q0aJFOnz5NbAIAAMCjAmNz2LBhrq9zcnL0zjvvaPHixerZs6eGDh1qyXAAAADwbTd9zebPP/+suXPnatmyZerbt682bNig0qVLWzUbAAAAfFyBsTlhwgStXr1agwYN0ubNmxUUFGTlXAAAACgCCozNt99+W8WLF9ebb76p6dOnu653Op2y2Wx8XCUAAAA8KjA2T548aeUcAAAAKIJu6eMqAQAAgFtBbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhjy87Odnp7iFvlcDjkcDhkt9tlt9stO+7Tidu07SR9/lvh915jJ26wF/fYi3vs5UbsxD324h57ce+fA6sqODjY22PI39sD/B5WRyYAAAB+H/4ZAAAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGNum9j817/+pWeeeUaPPvqot0cBAADAH8RobA4fPly1a9dWy5Yt812/YcMGhYWFqVmzZpoxY4YkKSQkxPU1AAAAigajsdmvXz8lJyfnuy4vL0/PPvuskpOTlZqaqpSUFO3bt8/kGAAAAPASo7EZERGhMmXK5LsuLS1NISEhCgkJUUBAgGJiYrRmzRqTYwAAAMBL/K0+4PHjx1WpUiXX5YoVK2rXrl06ffq0Xn31VX3//fd66623NGLECLePT0xMVGJioiQpJiZGsbGxlswtSaF3OSVds+x4voCduMde3GMv7rGXG7ET99iLe+zFvXPnzll6vODgYLfXWx6bBbn77rs1depUj/eLi4tTXFycBRPd6OD5w9p28rZ5T9Vt4ho7cYu9uMde3GMvN2In7rEX99iLOy+UKlVgAFrJ8t+ZChUqKDMz03X52LFjqlChgtVjAAAAwAKWx2bjxo11+PBhpaenKzc3V8uXL1enTp2sHgMAAAAWMBqb8fHx6ty5sw4cOKAGDRooKSlJ/v7+mjx5svr06aPIyEh1795dtWrVMjkGAAAAvMToazbnz5/v9voOHTqoQ4cOJg8NAACA2wCvpgUAAIAxxCYAAACMITYBAABgDLEJAAAAY4hNAAAAGENsAgAAwBhiEwAAAMYQmwAAADCG2AQAAIAxxCYAAACMITYBAABgDLEJAAAAY3wyNh0Oh0aOHCmHw+HtUQAAAHAT/t4e4Pew2+2y2+3eHgMAAAAe2LKzs53eHsJXJCYmKi4uzttj3FbYiXvsxT324h57uRE7cY+9uMde3Ltd9uKTT6N7S2JiordHuO2wE/fYi3vsxT32ciN24h57cY+9uHe77IXYBAAAgDHEJgAAAIy5Y+zYsRO8PYQvadiwobdHuO2wE/fYi3vsxT32ciN24h57cY+9uHc77IU3CAEAAMAYnkYHAACAMcTmTfz888+KjY1V8+bNFRsbqzNnzhR43/Pnz6tBgwZKSEiwcELvKMxeMjIy1LZtW0VHRysqKkoLFiywflALFWYn3333nTp37qyoqCi1bt1aKSkpXpjUWoX9O9SnTx+Fhoaqf//+Fk9onQ0bNigT9PsAAAyaSURBVCgsLEzNmjXTjBkzbrj98uXLevzxx9WsWTPZ7XYdOXLEC1Naz9Netm7dqrZt2yo4OFgrV670woTe4Wkvc+fOVWRkpFq3bq1evXopIyPDC1Naz9NeFixYoFatWik6OlpdunTRvn37vDCltTzt5FerVq1SuXLltHv3bgunu47YvImZM2eqZcuW2rlzp1q2bKmZM2cWeN/JkycrPDzcwum8pzB7KV++vNasWaNNmzZp7dq1mjlzprKysrwwrTUKs5MSJUpo9uzZ+vLLL7VkyRKNGzdOZ8+e9cK01ins36GnnnpKb7/9tsXTWScvL0/PPvuskpOTlZqaqpSUlBv+I7ho0SKVLl1aX331lYYMGaKJEyd6aVrrFGYv9913n2bNmqXevXt7aUrrFWYv9evX1/r167V582Z169ZNL7/8spemtU5h9tK7d29t2bJFmzZt0tNPP60XX3zRS9NaozA7kaScnBzNnz9fTZs29cKUxOZNrVmzRn379pUk9e3bV59++qnb++3Zs0fZ2dmKjo62cDrvKcxeAgICVLx4cUlSbm6url27ZumMVivMTkJDQxUaGipJCg4OVrly5XTq1ClL57RaYf8OtWrVSkFBQVaOZqm0tDSFhIQoJCREAQEBiomJ0Zo1a/Ld57e76tatm7744gs5nUX7JfWF2UuVKlVUt25d2Ww2L01pvcLsJSoqSiVKlJAkNW3aVMeOHfPGqJYqzF7uuusu19cXL14s8n9uCrMT6foJsaefftr132WrEZs3kZ2dreDgYEnXz9RlZ2ffcJ9r165p/Pjxf4p/Vf6qMHuRpMzMTLVu3VqNGjXS008/7XpMUVTYnfwqLS1Nubm5qlatmhXjec2t7qWoOn78uCpVquS6XLFiRR0/fjzffbKyslz38ff3V6lSpXT69GlL57RaYfbyZ3Sre1m0aJHatWtnxWheVdi9vPfee2rWrJlefvllvfrqq1aOaLnC7GTPnj3KzMxUx44drR7PxSc/G/2P1Lt3b508efKG659//vl8l202m9t/Ib3//vtq3769KlasaGxGb/i/7kWSKlWqpM2bNysrK0txcXHq1q2b7r33XiPzWuGP2Il0PSqGDRum2bNny8/P9/+990ftBcCtW7p0qfbs2aMVK1Z4e5TbxuDBgzV48GAtW7ZMb775pubMmePtkbzm1xNis2bN8uocf/rYXLZsWYG3lStXTllZWQoODlZWVpbKli17w32+/vprbd++XR988IEuXLig3NxclSxZUuPHjzc5tnH/1738VnBwsGrVqqXt27ere/fuf/SolvkjdnL+/Hn1799fzz//vB544AFTo1rqj/yzUlRVqFBBmZmZrsvHjh1ThQoV8t0nODhYmZmZqlixoq5evapz587p7rvvtnpUSxVmL39Ghd3L5s2bNX36dK1YscJrT49a6Vb/vPTs2VNjxoyxYjSv8bSTnJwc7d27VzExMZKkkydPauDAgUpKSlKjRo0sm9P3T6sY1KlTJy1ZskSStGTJEnXu3PmG+8ybN0+7d+9WWlqaJkyYoD59+vh8aHpSmL0cO3ZMly5dkiSdOXNGO3bsUI0aNSyd00qF2Ulubq4efvhh9enTx6ej+1YUZi9/Bo0bN9bhw4eVnp6u3NxcLV++XJ06dcp3n9/uatWqVYqKiiryZ4ILs5c/o8Ls5dtvv9Xo0aO1cOFClStXzkuTWqswezl48KDr6/Xr16t69epWj2kpTzspVaqU9u3bp7S0NKWlpalp06aWh6ZEbN7U8OHDtXnzZjVv3lxbtmzR8OHDJUm7d+/WiBEjvDyd9xRmL/v371enTp0UHR2tHj166Mknn1SdOnW8ObZRhdnJihUrtG3bNiUnJys6OlrR0dH67rvvvDm2cYX9O9S1a1cNHjxYX3zxhRo0aKCNGzd6a2Qj/P39NXnyZPXp00eRkZHq3r27atWqpddee01r166VJA0YMECnT59Ws2bNNHfu3CL/LlqpcHv55ptv1KBBA61atUqjR49WVFSUl6c2rzB7efnll3XhwgUNHjxY0dHRGjhwoJenNq8we3nvvfcUFRWl6OhozZ07V7Nnz/by1GYVZie3Az5BCAAAAMZwZhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCcDnVa1aNd/lxYsXKyEh4Za/T2pqqvr37+/2tnnz5unixYu/az5vSE1N1c6dO419/yNHjtz0B/oDwK+ITQAohPnz57s+qOB2dPXq1XyXU1NT9dVXXxk7XkZGBrEJoFCITQBFmsPhkN1uV5s2bfJ9jntqaqrrh+u3adNGOTk5kqQLFy7o0UcfVXh4uIYMGSKn06n58+crKytLPXv2dH3s25gxY9S+fXtFRUXp9ddfdx1v/fr1Cg8PV7t27fTcc8+5zpQWdLxfHTlyxHXMiIgIPfroo64zqXv27FH37t3Vrl07PfTQQ8rKypIk9ejRQy+88ILat2+v+fPn5/teCxYs0Lx58xQdHa3U1FQ1bdpUTqdTZ8+eVfny5bV161ZJUrdu3XTw4EFduHBBw4cPV8eOHdWmTRutWbNGkpSXl6cJEyaoQ4cOat26tf7xj39IkiZNmqTt27crOjpa8+bN+2N/0wAUKX/6z0YH4Pt++eUXRUdHuy6fOXNGdrtdktSiRQutXbtWNptNCxcu1OzZszVx4kS9/fbbev3119WiRQvl5OQoMDBQkvTdd9/pyy+/VHBwsLp06aIdO3YoPj5e8+bNU0pKiu655x5J0vPPP68yZcooLy9PvXr10v/+7/8qNDRUo0eP1sqVK1W1alXFx8e7ZiroeL914MABvfXWW2rRooWGDx+uDz74QPHx8XruueeUmJiosmXLKiUlRa+++qpmzpwpSbpy5Yo+++yzfN+nSpUqeuSRR1SyZEk9+eSTkqTQ0FDt27dPR44cUYMGDbR9+3Y1bdpUx44dU2hoqF555RW1bNlSM2fO1NmzZ9WxY0e1atVKy5Yt01133aX169fr8uXL6tKli6Kjo/Xiiy9qzpw5+vDDD/+430gARRKxCcDnBQYGatOmTa7Lixcv1u7duyVJx44d0xNPPKETJ04oNzdXVapUkSQ1b95c48ePV+/evdW1a1cFBQVJkpo0aaKKFStKkurVq6eMjAyFhYXdcMwVK1YoMTFReXl5OnHihPbv369r166patWqrteQ9urVS4mJiTc93m9VqlRJLVq0kCQ99NBDevfdd9W2bVv9+OOPio2NlSRdu3ZN5cuXdz2mR48ehdpRWFiYtm3bpiNHjuiZZ57RwoULFRER4fqM5E2bNsnhcGjOnDmSpMuXLyszM1Off/65fvjhB61atUqSdP78eR06dEgBAQGFOi4AEJsAirTnnntOQ4cOVadOnZSamqopU6ZIkp555hl16NBBn332mbp06aKPPvpIkvJFlJ+f3w2vhZSk9PR0zZkzR+vXr1fp0qX11FNP6ZdffrnpHO6O99e//jXffWw22w2XnU6natWq5Xpa+z+VLFnS8xIkhYeHa8GCBcrKylJCQoLmzJmj1NRUV0g7nU598MEHqlGjRr7HOZ1OTZ48WW3bts13fWpqaqGOCwC8ZhNAkXbu3DlVqFBBkpScnOy6/vDhw6pTp46GDx+uRo0a6aeffrrp9wkKCnK9zvL8+fMqWbKkSpUqpZMnT2rjxo2SpBo1aig9PV1HjhyRJC1fvvyWjnf06FHXm3qWLVumFi1aqEaNGjp16pTr+itXrmjv3r0ef92/nVe6fsb2q6++kp+fnwIDA1WvXj0lJiYqPDxcktSmTRu9++67cjqdkqRvv/1WktS2bVstWLBAV65ckSTX6zv/8/sDQEGITQBF2tixYzV48GC1a9fO9XpLSXrnnXfUsmVLtW7dWsWKFVO7du1u+n0GDRqkvn37KiYmRvXq1VO9evVcb+hp3ry5JOnOO+/UlClT1LdvX7Vr105BQUEqVapUoY9Xo0YNvf/++4qIiNCZM2f0yCOPKCAgQO+//74mTpzoenNRYd5lbrfb9emnnyo6Olrbtm1T8eLFVbFiRTVt2lTS9afVc3JyVKdOHUnSqFGjdPXqVbVu3VpRUVF67bXXJEkDBw5UzZo11a5dO7Vs2VKjRo1SXl6e6tSpozvuuIM3CAHwyJadne309hAAUFTk5OQoKChITqdTCQkJql69uoYMGeLxcUeOHNGAAQP0xRdfWDAlAFiHM5sA8AdKSkpSdHS0oqKidO7cOcXFxXl7JADwKs5sAgAAwBjObAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYMz/A1yR2074noLXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 792x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDMLUKHJGrDh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b0026fd3-1f13-4299-8448-5e5d0cc4e1c8"
      },
      "source": [
        "hashtag_summary['top_hashtags'][:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcCIfwJfGrDm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "outputId": "16f4f1dc-12c0-4fee-f5f9-dc422dc12aee"
      },
      "source": [
        "plt.figure(facecolor='#ebebeb', figsize=(8, 12))\n",
        "plt.barh([x[0] for x in hashtag_summary['top_hashtags'][2:][:5]][::-1],\n",
        "         [x[1] for x in hashtag_summary['top_hashtags'][2:][:5]][::-1])\n",
        "plt.title('Top Hashtags')\n",
        "# plt.xticks(range(3))\n",
        "plt.grid(alpha=0.5)\n",
        "plt.gca().set_frame_on(False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t7c1RRx3Vf/7zn/98brvttpx00kmNjgMAQ0rDIT/99NOzadOmbN68OW+88UZWrlyZjo6OAWc6OjqyYsWKJL99CX3SpElpamrKrl27ctFFF2XBggX5+Mc/3ugoADDkNBzy5ubm3HjjjbngggsyceLEzJgxI2PGjMmiRYvy4x//OEly8cUXZ/v27Wlvb8/tt9+eBQsWJEm+973vZdOmTVm8eHGmTp2aqVOnpqenp9GRAGDIaOrp6dk72EPw7rq7u31PqEF22Dg7bJwdNs4OB/KT3QCgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgsA8k5A8++GAmTJiQ9vb23Hzzzfvc7+vry5w5c9Le3p7p06fnxRdf7L/3rW99K+3t7ZkwYULWrFnzQYwDAENGwyHfs2dPrr766ixfvjzr16/P3XffnY0bNw44s2zZsgwfPjyPP/545s6dm4ULFyZJNm7cmJUrV+YnP/lJVqxYkauuuip79uxpdCQAGDIaDvmGDRvS1taWtra2HHrooZk5c2a6uroGnOnq6sqsWbOSJOeee27WrVuXvXv3pqurKzNnzsywYcMyatSotLW1ZcOGDY2OBABDRsMh37ZtW0444YT+69bW1mzbtm3Ame7u7v4zzc3NOfLII7N9+/b39L4AwDtrHuwB3qulS5dm6dKlSZKZM2ems7NzkCfav3p7ewd7hPLssHF22Dg7bNxQ3GFLS8s73ms45CNHjsyWLVv6r7du3ZqRI0fuM8CWLVvS2tqa3bt3p7e3N0cdddR7et+3zJ49O7Nnz2503NL+0CeS98YOG2eHjbPDxtnh7zT80vrpp5+eTZs2ZfPmzXnjjTeycuXKdHR0DDjT0dGRFStWJElWrVqVSZMmpampKR0dHVm5cmX6+vqyefPmbNq0KePGjWt0JAAYMhr+iry5uTk33nhjLrjggrz55pu58MILM2bMmCxatCinnXZaOjo6cvHFF2fevHlpb2/PiBEj8t3vfjdJMmbMmMyYMSOTJk3KwQcfnEWLFuXggw9u+EkBwFDR1NPTs3ewh+DddXd3eympQXbYODtsnB02zg4H8pPdAKAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwhoK+Y4dO9LZ2Znx48ens7MzO3fufNtzy5cvz/jx4zN+/PgsX748SfKb3/wmF154Yc4888xMmjQpCxcubGQUABiSGgr5LbfcksmTJ+exxx7L5MmTc8stt+xzZseOHVm8eHHuv//+rF69OosXL+4P/pe+9KU8+uijWbNmTR577LE88MADjYwDAENOQyHv6urKrFmzkiSzZs3Kfffdt8+Zhx56KFOmTMmIESMyfPjwTJkyJWvWrMmHPvShTJo0KUly6KGHZuzYsdm2bVsj4wDAkNPcyDv39PSkpaUlSXL88cenp6dnnzPbtm1La2tr/3Vra+s+wd61a1dWr16dSy+99B0fa+nSpVm6dGmSZObMmens7Gxk9HJ6e3sHe4Ty7LBxdtg4O2zcUNzhW619O+8a8vPPPz8vv/zyPm+/5pprBlw3NTWlqanpjx5u9+7dufTSSzNnzpy0tbW947nZs2dn9uzZf/TH/3Pyhz6RvDd22Dg7bJwdNs4Of+ddQ37XXXe9471jjz023d3daWlpSXd3d4455ph9zowcOTLr16/vv966dWsmTpzYfz1//vycfPLJmTt37h87OwAMeQ19j7yjoyMrVqxIkqxYsSLnnHPOPmfOOuusrF27Njt37szOnTuzdu3anHXWWUmSG264Ib29vbn++usbGQMAhqyGQn755Zfn4Ycfzvjx4/PII4/k8ssvT5I89dRTueKKK5IkI0aMyPz58zNt2rRMmzYtV155ZUaMGJGtW7dmyZIlee655/LJT34yU6dOzZ133tn4MwKAIaSpp6dn72APwbt761sYvH922Dg7bJwdNs4OB/KT3QCgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMKEHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoDAhB4DChBwAChNyAChMyAGgMCEHgMIaCvmOHTvS2dmZ8ePHp7OzMzt37nzbc8uXL8/48eMzfvz4LF++fJ/7n/vc5zJ58uRGRgGAIamhkN9yyy2ZPHlyHnvssUyePDm33HLLPmd27NiRxYsX5/7778/q1auzePHiAcG/9957c/jhhzcyBgAMWQ2FvKurK7NmzUqSzJo1K/fdd98+Zx566KFMmTIlI0aMyPDhwzNlypSsWbMmSfLaa6/l9ttvz/z58xsZAwCGrOZG3rmnpyctLS1JkuOPPz49PT37nNm2bVtaW1v7r1tbW7Nt27YkyaJFizJv3rwcdthh7/pYS5cuzdKlS5MkM2fOTGdnZyOjl9Pb2zvYI5Rnh42zw8bZYeOG4g7fau3bedeQn3/++Xn55Zf3efs111wz4LqpqSlNTU3veaif//zn+eUvf5lvfOMbefHFF9/1/OzZszN79uz3/PH/HP2hTyTvjR02zg4bZ4eNs8PfedeQ33XXXe9479hjj013d3daWlrS3d2dY445Zp8zI0eOzPr16/uvt27dmokTJ+aJJ57IU089lXHjxmX37t155ZVXct555+Wee+55n08FAIaehr5H3tHRkRUrViRJVqxYkXPOOWefM2eddVbWrl2bnTt3ZufOnVm7dm3OOuusfOELX8jTTz+dDRs25N57783o0aNFHAD+SA2F/PLLL8/DDz+c8ePH55FHHsnll1+eJHnqqadyxRVXJElGjBiR+fPnZ9q0aZk2bVquvPLKjBgxovHJAYA09fT07B3sIXh3b30Lg/fPDhtnh42zw8bZ4UB+shsAFCbkAFCYkANAYUIOAIUJOQAUJuQAUJiQA0BhQg4AhQk5ABQm5ABQmJADQGFCDgCFCTkAFCbkAFCYkANAYUIOAIUJOQAUJuQAUJiQA0BhQg4AhQk5ABQm5ABQmJADQGFCDgCFCTkAFCbkAFCYkANAYUIOAIUJOQAUJuQAUJiQA0BhQg4AhQk5ABQm5ABQmJADQGFCDgCFCTkAFCbkAFCYkANAYUIOAIUJOQAUJuQAUJiQA0BhQg4AhQk5ABQm5ABQmJADQGFCDgCFCTkAFCbkAFCYkANAYUIOAIUJOQAUJuQAUJiQA0BhQg4AhQk5ABQm5ABQmJADQGFCDgCFCTkAFCbkAFCYkANAYUIOAIUJOQAUJuQAUJiQA0BhQg4AhQk5ABQm5ABQmJADQGFCDgCFCTkAFCbkAFCYkANAYUIOAIUJOQAUJuQAUJiQA0BhQg4AhQk5ABQm5ABQmJADQGFCXsTq1asHe4Ty7LBxdtg4O2ycHQ4k5EUsXbp0sEcozw4bZ4eNs8PG2eFAQg4AhQk5ABR28Fe/+tWvD/YQvDcf+9jHBnuE8uywcXbYODtsnB3+TlNPT8/ewR4CAHh/vLQOAIUJOQAUJuQHiB07dqSzszPjx49PZ2dndu7c+bbnli9fnvHjx2f8+PFZvnz5Pvc/97nPZfLkyX/qcQ9YjezxN7/5TS688MKceeaZmTRpUhYuXLg/Rx90Dz74YCZMmJD29vbcfPPN+9zv6+vLnDlz0t7enunTp+fFF1/sv/etb30r7e3tmTBhQtasWbM/xz6gvN8drl27NmeffXY+8YlP5Oyzz866dev29+gHjEb+PkySl156KaNGjcq3v/3t/TXyoBPyA8Qtt9ySyZMn57HHHsvkyZNzyy237HNmx44dWbx4ce6///6sXr06ixcvHhCqe++9N4cffvj+HPuA0+gev/SlL+XRRx/NmjVr8thjj+WBBx7Y309hUOzZsydXX311li9fnvXr1+fuu+/Oxo0bB5xZtmxZhg8fnscffzxz587t/43Oxo0bs3LlyvzkJz/JihUrctVVV2XPnj2D8TQGVSM7POqoo7Js2bI88sgjue222zJv3rzBeAqDrpEdvmXBggU5++yz9+fYg07IDxBdXV2ZNWtWkmTWrFm577779jnz0EMPZcqUKRkxYkSGDx+eKVOm9H/189prr+X222/P/Pnz9+vcB5pG9vihD30okyZNSpIceuihGTt2bLZt27Zf5x8sGzZsSFtbW9ra2nLooYdm5syZ6erqGnDm93d77rnnZt26ddm7d2+6uroyc+bMDBs2LKNGjUpbW1s2bNgwGE9jUDWyw7Fjx6alpSVJMmbMmLz++uvp6+vb789hsDWywyS57777MmrUqIwZM2a/zz6YhPwA0dPT0/8P8vHHH5+enp59zmzbti2tra39162trf2hWbRoUebNm5fDDjts/wx8gGp0j2/ZtWtXVq9ePWS+TbFt27accMIJ/ddvt5Pu7u7+M83NzTnyyCOzffv29/S+Q0EjO/x9q1atytixYzNs2LA//dAHmEZ2+Nprr+XWW2/NV77ylf0684GgebAHGErOP//8vPzyy/u8/Zprrhlw3TQ5R6AAAALjSURBVNTUlKampvf8cX/+85/nl7/8Zb7xjW/s8/2iP0d/qj2+Zffu3bn00kszZ86ctLW1vd8x4Y/27LPP5rrrrssPf/jDwR6lnJtuuimXXXZZjjjiiMEeZb8T8v3orrvuesd7xx57bLq7u9PS0pLu7u4cc8wx+5wZOXJk1q9f33+9devWTJw4MU888USeeuqpjBs3Lrt3784rr7yS8847L/fcc8+f5HkMtj/VHt8yf/78nHzyyZk7d+4HO/gBbOTIkdmyZUv/9datWzNy5MgBZ1paWrJly5a0trZm9+7d6e3tzVFHHfWe3ncoaGSHb53//Oc/n9tuuy0nnXTSfp39QNHIDp988smsWrUqCxcuzK5du3LQQQdl2LBhmTNnzv5+Gvudl9YPEB0dHVmxYkWSZMWKFTnnnHP2OXPWWWdl7dq12blzZ3bu3Jm1a9fmrLPOyhe+8IU8/fTT2bBhQ+69996MHj36zzbi76aRPSbJDTfckN7e3lx//fX7de7Bdvrpp2fTpk3ZvHlz3njjjaxcuTIdHR0Dzvz+bletWpVJkyalqakpHR0dWblyZfr6+rJ58+Zs2rQp48aNG4ynMaga2eGuXbty0UUXZcGCBfn4xz8+GOMfEBrZ4b333psNGzZkw4YNueyyy3LFFVcMiYgnQn7AuPzyy/Pwww9n/PjxeeSRR3L55ZcnSZ566qlcccUVSZIRI0Zk/vz5mTZtWqZNm5Yrr7wyI0aMGMyxDziN7HHr1q1ZsmRJnnvuuXzyk5/M1KlTc+eddw7m09lvmpubc+ONN+aCCy7IxIkTM2PGjIwZMyaLFi3Kj3/84yTJxRdfnO3bt6e9vT233357FixYkOS3/3HWjBkzMmnSpMyaNSuLFi3KwQcfPJhPZ1A0ssPvfe972bRpUxYvXpypU6dm6tSpb/vfd/y5a2SHQ5kf0QoAhfmKHAAKE3IAKEzIAaAwIQeAwoQcAAoTcgAoTMgBoLD/BxhgYF319R5iAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5YUuqGkrGrDp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "10229d7e-4e66-4859-dda1-b4468b180b81"
      },
      "source": [
        "emoji_summary = adv.extract_emoji(tweets_df['Tweets'])\n",
        "emoji_summary.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['emoji', 'emoji_text', 'emoji_flat', 'emoji_flat_text', 'emoji_counts', 'emoji_freq', 'top_emoji', 'top_emoji_text', 'top_emoji_groups', 'top_emoji_sub_groups', 'overview'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8tpiBK0GrDs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4bab7c31-3ea1-4f95-e5ec-b183fb076384"
      },
      "source": [
        "emoji_summary['overview']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'emoji_per_post': 1.15, 'num_emoji': 23, 'num_posts': 20, 'unique_emoji': 14}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_KbiZTfGrDw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "aa550026-6fd6-4d98-c7ef-e9cc742be443"
      },
      "source": [
        "emoji_summary['emoji'][50:80]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vBQAxJeGrDz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "517cb2e3-abb6-473c-9b1d-d9ff091dfafd"
      },
      "source": [
        "emoji_summary['emoji_text'][50:80]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kny_YTO8GrD1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f7bacd24-27b5-4370-cae2-0198b52f4ca8"
      },
      "source": [
        "emoji_summary['emoji_flat'][:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['😍', '\\U0001f97a', '🙏', '🍔', '😋', '🤣', '🤣', '🤣', '🤣', '🤣']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_0OcMZXGrD6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "296fae10-b5a9-49b3-cce8-b76cc4c34dcd"
      },
      "source": [
        "emoji_summary['emoji_flat_text'][:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['smiling face with heart-eyes',\n",
              " 'pleading face',\n",
              " 'folded hands',\n",
              " 'hamburger',\n",
              " 'face savoring food',\n",
              " 'rolling on the floor laughing',\n",
              " 'rolling on the floor laughing',\n",
              " 'rolling on the floor laughing',\n",
              " 'rolling on the floor laughing',\n",
              " 'rolling on the floor laughing']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gw47wHBJGrD9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "41d8045e-9be9-4915-a18e-896ee3b0626d"
      },
      "source": [
        "list(zip(emoji_summary['emoji_flat'][:10], emoji_summary['emoji_flat_text'][:10]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('😍', 'smiling face with heart-eyes'),\n",
              " ('\\U0001f97a', 'pleading face'),\n",
              " ('🙏', 'folded hands'),\n",
              " ('🍔', 'hamburger'),\n",
              " ('😋', 'face savoring food'),\n",
              " ('🤣', 'rolling on the floor laughing'),\n",
              " ('🤣', 'rolling on the floor laughing'),\n",
              " ('🤣', 'rolling on the floor laughing'),\n",
              " ('🤣', 'rolling on the floor laughing'),\n",
              " ('🤣', 'rolling on the floor laughing')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "runlNSfHGrEB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "30642476-a7f2-4bee-e3af-9444a33e8aac"
      },
      "source": [
        "emoji_summary['emoji_counts'][:15]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 0, 0, 0, 0, 1, 4, 0, 7, 1, 0, 1, 2, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGTxPQsFGrED",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ade2ebed-590d-4f3f-9e61-f590ec13e782"
      },
      "source": [
        "emoji_summary['emoji_freq'][:15]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 12), (1, 3), (2, 2), (4, 1), (5, 1), (7, 1)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5I8nB82wGrEG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "70c5e85d-eb6e-4433-d8d8-b5971eee2ee5"
      },
      "source": [
        "plt.figure(facecolor='#ebebeb', figsize=(8, 8))\n",
        "plt.bar([x[0] for x in emoji_summary['emoji_freq'][:15]],\n",
        "        [x[1] for x in emoji_summary['emoji_freq'][:15]])\n",
        "plt.title('Emoji frequency')\n",
        "plt.xlabel('Emoji per tweet')\n",
        "plt.ylabel('Number of tweets')\n",
        "plt.yscale('log')\n",
        "plt.grid(alpha=0.5)\n",
        "plt.gca().set_frame_on(False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NfgaWrSjGrEJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "b398d457-31f8-4593-f3c2-4c4e05d00e5c"
      },
      "source": [
        "emoji_summary['top_emoji'][:8]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('🤣', 7),\n",
              " ('👏', 3),\n",
              " ('🙏', 2),\n",
              " ('😍', 1),\n",
              " ('\\U0001f97a', 1),\n",
              " ('🍔', 1),\n",
              " ('😋', 1),\n",
              " ('🤦🏻\\u200d♂️', 1)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQXFyGqwGrEN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "7d070c6b-3d81-4566-d166-6f624c06574d"
      },
      "source": [
        "emoji_summary['top_emoji_text'][:8]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('rolling on the floor laughing', 7),\n",
              " ('clapping hands', 3),\n",
              " ('folded hands', 2),\n",
              " ('smiling face with heart-eyes', 1),\n",
              " ('pleading face', 1),\n",
              " ('hamburger', 1),\n",
              " ('face savoring food', 1),\n",
              " ('man facepalming: light skin tone', 1)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDAf-JyfGrEP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "1bf667ac-15ff-44a0-b963-441ff11a28c8"
      },
      "source": [
        "plt.figure(facecolor='#ebebeb', figsize=(8, 8))\n",
        "plt.barh([x[0] for x in emoji_summary['top_emoji_text'][:8]][::-1],\n",
        "         [x[1] for x in emoji_summary['top_emoji_text'][:8]][::-1])\n",
        "plt.title('Top Emoji')\n",
        "plt.grid(alpha=0.5)\n",
        "plt.gca().set_frame_on(False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBuvxltgGrET",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f089602b-55d0-4898-80c2-48a84b3e5c67"
      },
      "source": [
        "mention_summary = adv.extract_mentions(tweets_df.Tweets)\n",
        "mention_summary.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['mentions', 'mentions_flat', 'mention_counts', 'mention_freq', 'top_mentions', 'overview'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aibfhLKsGrEW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "2995eee9-1276-40c2-cf7b-88da46dd9561"
      },
      "source": [
        "mention_summary['overview']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mentions_per_post': 0.85,\n",
              " 'num_mentions': 17,\n",
              " 'num_posts': 20,\n",
              " 'unique_mentions': 17}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WAVuQkuzGrEY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "91b1a379-81c0-4f93-da5e-88659262ac3d"
      },
      "source": [
        "mention_summary['mentions'][:15]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[],\n",
              " [],\n",
              " [],\n",
              " ['@untkcqr', '@emilia14614784', '@luvwaifus'],\n",
              " ['@ohohuh1'],\n",
              " ['@sobrinov_edu'],\n",
              " ['@alelegabi'],\n",
              " ['@gordoomx'],\n",
              " [],\n",
              " [],\n",
              " ['@melody260515'],\n",
              " ['@daniloruiz1924', '@universitario'],\n",
              " [],\n",
              " [],\n",
              " ['@cin_deli']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQhMYNGTGrEc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "4c1a1c2b-af7e-4e1b-8473-2352d800177c"
      },
      "source": [
        "mention_summary['mentions_flat'][:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['@untkcqr',\n",
              " '@emilia14614784',\n",
              " '@luvwaifus',\n",
              " '@ohohuh1',\n",
              " '@sobrinov_edu',\n",
              " '@alelegabi',\n",
              " '@gordoomx',\n",
              " '@melody260515',\n",
              " '@daniloruiz1924',\n",
              " '@universitario']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHlbSDfoGrEe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5a6b7c96-3b24-4493-fa21-776bb205ef22"
      },
      "source": [
        "mention_summary['mention_counts'][:20]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 0, 3, 1, 1, 1, 1, 0, 0, 1, 2, 0, 0, 1, 0, 2, 2, 1, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1cnepuYGrEg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6dd840cf-dddc-494f-b24f-e74052e355de"
      },
      "source": [
        "mention_summary['mention_freq'][:15]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 8), (1, 8), (2, 3), (3, 1)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbJlkp0tGrEj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "e28dee7c-2f9e-49c5-f7f6-4804c9894d9d"
      },
      "source": [
        "plt.figure(facecolor='#ebebeb', figsize=(8, 8))\n",
        "plt.bar([x[0] for x in mention_summary['mention_freq'][:15]],\n",
        "        [x[1] for x in mention_summary['mention_freq'][:15]])\n",
        "plt.title('Mention frequency')\n",
        "plt.xlabel('Mention per tweet')\n",
        "plt.ylabel('Number of tweets')\n",
        "plt.grid(alpha=0.5)\n",
        "plt.yscale('log')\n",
        "plt.gca().set_frame_on(False)\n",
        "plt.savefig('snapshot/Mention Frequency.png');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Pw8z76HGrEn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "8c986dd8-a85f-4e1e-ff4a-ef64bb9ab6e8"
      },
      "source": [
        "mention_summary['top_mentions'][:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('@untkcqr', 1),\n",
              " ('@emilia14614784', 1),\n",
              " ('@luvwaifus', 1),\n",
              " ('@ohohuh1', 1),\n",
              " ('@sobrinov_edu', 1),\n",
              " ('@alelegabi', 1),\n",
              " ('@gordoomx', 1),\n",
              " ('@melody260515', 1),\n",
              " ('@daniloruiz1924', 1),\n",
              " ('@universitario', 1)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1vc7iY9GrEp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "bde27274-6c93-4942-d074-5eb6f7fda164"
      },
      "source": [
        "plt.figure(facecolor='#ebebeb', figsize=(8, 8))\n",
        "plt.barh([x[0] for x in mention_summary['top_mentions'][:15]][::-1],\n",
        "         [x[1] for x in mention_summary['top_mentions'][:15]][::-1])\n",
        "plt.title('Top Mentions')\n",
        "plt.grid(alpha=0.5)\n",
        "plt.gca().set_frame_on(False)\n",
        "plt.savefig('snapshot/Top Mentions.png');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzAtCJKhGrEu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "2629cec7-2ef5-4888-f466-b0ec0805d3c3"
      },
      "source": [
        "tweets_df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'Tweets', 'location', 'followers', 'word_count', 'char_count',\n",
              "       'avg_word', 'hastags', 'numerics', 'Tweets_clean', 'ACTIONS', 'NOUNS'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwKUY9mYGrEx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "4cc0f2e7-82ad-44ee-ccb7-784dfd683b37"
      },
      "source": [
        "extracted_tweets =  (tweets_df[['Tweets', 'followers']]\n",
        " .assign(hashtags=hashtag_summary['hashtags'],\n",
        "         hashcounts=hashtag_summary['hashtag_counts'],\n",
        "         mentions=mention_summary['mentions'],\n",
        "         mention_count=mention_summary['mention_counts'],\n",
        "         emoji=emoji_summary['emoji'],\n",
        "         emoji_text=emoji_summary['emoji_text'],\n",
        "         emoji_count=emoji_summary['emoji_counts'],))\n",
        "extracted_tweets.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tweets</th>\n",
              "      <th>followers</th>\n",
              "      <th>hashtags</th>\n",
              "      <th>hashcounts</th>\n",
              "      <th>mentions</th>\n",
              "      <th>mention_count</th>\n",
              "      <th>emoji</th>\n",
              "      <th>emoji_text</th>\n",
              "      <th>emoji_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Poco a poco todo se va acomodando</td>\n",
              "      <td>170</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hay gente que han perdido la vergüenza, pretender ser candidato de UPP.</td>\n",
              "      <td>51</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Éste Viernes de Sankirtana, nos visita Hanumat Prana Das, un gran y entusiasta sankirtanero. \\nPerú /México /Colombi...</td>\n",
              "      <td>401</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>@UnTkCqr @Emilia14614784 @LuvWaifus Cuídate mucho Anja. Recuerda que Emilia es la Best Girl de Re:Zero por muchas r…...</td>\n",
              "      <td>371</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>[@untkcqr, @emilia14614784, @luvwaifus]</td>\n",
              "      <td>3</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>@ohohuh1 :( cuídate mucho.\\n\\nQué desgracia, todo...</td>\n",
              "      <td>196</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>[@ohohuh1]</td>\n",
              "      <td>1</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                    Tweets  ...  emoji_count\n",
              "0                                                                                        Poco a poco todo se va acomodando  ...            0\n",
              "1                                                  Hay gente que han perdido la vergüenza, pretender ser candidato de UPP.  ...            0\n",
              "2  Éste Viernes de Sankirtana, nos visita Hanumat Prana Das, un gran y entusiasta sankirtanero. \\nPerú /México /Colombi...  ...            0\n",
              "3  @UnTkCqr @Emilia14614784 @LuvWaifus Cuídate mucho Anja. Recuerda que Emilia es la Best Girl de Re:Zero por muchas r…...  ...            0\n",
              "4                                                                     @ohohuh1 :( cuídate mucho.\\n\\nQué desgracia, todo...  ...            0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qI_rHEfEGrEz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "e20083ea-6557-4c62-a77b-7eadb2b8c244"
      },
      "source": [
        "extracted_tweets.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Tweets', 'followers', 'hashtags', 'hashcounts', 'mentions',\n",
              "       'mention_count', 'emoji', 'emoji_text', 'emoji_count'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_GRXb6oGrE1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "29674214-d82b-43e2-fb83-ee99bd05367c"
      },
      "source": [
        "word_freq_hash = adv.word_frequency(extracted_tweets['hashtags'].str.join(' '), \n",
        "                                    extracted_tweets['followers'].fillna(0))#.sort_values(['abs_freq'], ascending=False).head(20)\n",
        "word_freq_hash.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word</th>\n",
              "      <th>abs_freq</th>\n",
              "      <th>wtd_freq</th>\n",
              "      <th>rel_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [word, abs_freq, wtd_freq, rel_value]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2Yar4ZhGrE3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "473d7456-a1db-4b1b-9244-7ca8e794f4d7"
      },
      "source": [
        "extracted_tweets[extracted_tweets['hashtags'].str.join(' ')\n",
        "                  .str.contains('lima',case=False)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tweets</th>\n",
              "      <th>followers</th>\n",
              "      <th>hashtags</th>\n",
              "      <th>hashcounts</th>\n",
              "      <th>mentions</th>\n",
              "      <th>mention_count</th>\n",
              "      <th>emoji</th>\n",
              "      <th>emoji_text</th>\n",
              "      <th>emoji_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Tweets, followers, hashtags, hashcounts, mentions, mention_count, emoji, emoji_text, emoji_count]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxiJ0t74GrE5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "26d556f6-c43a-465b-bca4-68e82785668b"
      },
      "source": [
        "word_freq_mention = adv.word_frequency(extracted_tweets['mentions'].str.join(' '), \n",
        "                                       extracted_tweets['followers'].fillna(0))\n",
        "                                    #.sort_values(['abs_freq'], ascending=False).head(20)\n",
        "word_freq_mention.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word</th>\n",
              "      <th>abs_freq</th>\n",
              "      <th>wtd_freq</th>\n",
              "      <th>rel_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>@daniloruiz1924</td>\n",
              "      <td>1</td>\n",
              "      <td>6014</td>\n",
              "      <td>6014.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>@universitario</td>\n",
              "      <td>1</td>\n",
              "      <td>6014</td>\n",
              "      <td>6014.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>@gordoomx</td>\n",
              "      <td>1</td>\n",
              "      <td>2161</td>\n",
              "      <td>2161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>@manago72</td>\n",
              "      <td>1</td>\n",
              "      <td>2161</td>\n",
              "      <td>2161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>@emilia14614784</td>\n",
              "      <td>1</td>\n",
              "      <td>371</td>\n",
              "      <td>371.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>@untkcqr</td>\n",
              "      <td>1</td>\n",
              "      <td>371</td>\n",
              "      <td>371.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>@luvwaifus</td>\n",
              "      <td>1</td>\n",
              "      <td>371</td>\n",
              "      <td>371.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>@marlovilela</td>\n",
              "      <td>1</td>\n",
              "      <td>329</td>\n",
              "      <td>329.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>@seleccionperu</td>\n",
              "      <td>1</td>\n",
              "      <td>329</td>\n",
              "      <td>329.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>@ohohuh1</td>\n",
              "      <td>1</td>\n",
              "      <td>196</td>\n",
              "      <td>196.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              word  abs_freq  wtd_freq  rel_value\n",
              "0  @daniloruiz1924         1      6014     6014.0\n",
              "1   @universitario         1      6014     6014.0\n",
              "2        @gordoomx         1      2161     2161.0\n",
              "3        @manago72         1      2161     2161.0\n",
              "4  @emilia14614784         1       371      371.0\n",
              "5         @untkcqr         1       371      371.0\n",
              "6       @luvwaifus         1       371      371.0\n",
              "7     @marlovilela         1       329      329.0\n",
              "8   @seleccionperu         1       329      329.0\n",
              "9         @ohohuh1         1       196      196.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZ71uAioGrE8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "5b929965-0714-442d-827c-c381022d2c8f"
      },
      "source": [
        "word_freq_emoji = adv.word_frequency(extracted_tweets['emoji'].str.join(' '), \n",
        "                                       extracted_tweets['followers'].fillna(0))#.sort_values(['abs_freq'], ascending=False).head(20)\n",
        "word_freq_emoji.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word</th>\n",
              "      <th>abs_freq</th>\n",
              "      <th>wtd_freq</th>\n",
              "      <th>rel_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>👏</td>\n",
              "      <td>3</td>\n",
              "      <td>6483</td>\n",
              "      <td>2161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>🙏</td>\n",
              "      <td>2</td>\n",
              "      <td>4322</td>\n",
              "      <td>2161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>🥺</td>\n",
              "      <td>1</td>\n",
              "      <td>2161</td>\n",
              "      <td>2161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>🍔</td>\n",
              "      <td>1</td>\n",
              "      <td>2161</td>\n",
              "      <td>2161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>😋</td>\n",
              "      <td>1</td>\n",
              "      <td>2161</td>\n",
              "      <td>2161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>🌛</td>\n",
              "      <td>1</td>\n",
              "      <td>2161</td>\n",
              "      <td>2161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>🤣</td>\n",
              "      <td>7</td>\n",
              "      <td>1946</td>\n",
              "      <td>278.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>😍</td>\n",
              "      <td>1</td>\n",
              "      <td>155</td>\n",
              "      <td>155.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>🤦🏻‍♂️</td>\n",
              "      <td>1</td>\n",
              "      <td>136</td>\n",
              "      <td>136.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>😪</td>\n",
              "      <td>1</td>\n",
              "      <td>135</td>\n",
              "      <td>135.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    word  abs_freq  wtd_freq  rel_value\n",
              "0      👏         3      6483     2161.0\n",
              "1      🙏         2      4322     2161.0\n",
              "2      🥺         1      2161     2161.0\n",
              "3      🍔         1      2161     2161.0\n",
              "4      😋         1      2161     2161.0\n",
              "5      🌛         1      2161     2161.0\n",
              "6      🤣         7      1946      278.0\n",
              "7      😍         1       155      155.0\n",
              "8  🤦🏻‍♂️         1       136      136.0\n",
              "9      😪         1       135      135.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHIxbDOcGrFA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "4d21763c-e07f-40b4-fffd-119dbb38212c"
      },
      "source": [
        "[adv.extract_emoji(k)['emoji_flat_text'][0] for k in word_freq_emoji['word'][:10]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['clapping hands',\n",
              " 'folded hands',\n",
              " 'pleading face',\n",
              " 'hamburger',\n",
              " 'face savoring food',\n",
              " 'first quarter moon face',\n",
              " 'rolling on the floor laughing',\n",
              " 'smiling face with heart-eyes',\n",
              " 'person facepalming',\n",
              " 'sleepy face']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2KTfLl9GrFC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "622abc67-815e-4313-8e0c-26ac1086562b"
      },
      "source": [
        "word_freq_emoji[:10].assign(emoji_text=[adv.extract_emoji(k)['emoji_flat_text'][0] for k in word_freq_emoji['word'][:10]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word</th>\n",
              "      <th>abs_freq</th>\n",
              "      <th>wtd_freq</th>\n",
              "      <th>rel_value</th>\n",
              "      <th>emoji_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>👏</td>\n",
              "      <td>3</td>\n",
              "      <td>6483</td>\n",
              "      <td>2161.0</td>\n",
              "      <td>clapping hands</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>🙏</td>\n",
              "      <td>2</td>\n",
              "      <td>4322</td>\n",
              "      <td>2161.0</td>\n",
              "      <td>folded hands</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>🥺</td>\n",
              "      <td>1</td>\n",
              "      <td>2161</td>\n",
              "      <td>2161.0</td>\n",
              "      <td>pleading face</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>🍔</td>\n",
              "      <td>1</td>\n",
              "      <td>2161</td>\n",
              "      <td>2161.0</td>\n",
              "      <td>hamburger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>😋</td>\n",
              "      <td>1</td>\n",
              "      <td>2161</td>\n",
              "      <td>2161.0</td>\n",
              "      <td>face savoring food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>🌛</td>\n",
              "      <td>1</td>\n",
              "      <td>2161</td>\n",
              "      <td>2161.0</td>\n",
              "      <td>first quarter moon face</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>🤣</td>\n",
              "      <td>7</td>\n",
              "      <td>1946</td>\n",
              "      <td>278.0</td>\n",
              "      <td>rolling on the floor laughing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>😍</td>\n",
              "      <td>1</td>\n",
              "      <td>155</td>\n",
              "      <td>155.0</td>\n",
              "      <td>smiling face with heart-eyes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>🤦🏻‍♂️</td>\n",
              "      <td>1</td>\n",
              "      <td>136</td>\n",
              "      <td>136.0</td>\n",
              "      <td>person facepalming</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>😪</td>\n",
              "      <td>1</td>\n",
              "      <td>135</td>\n",
              "      <td>135.0</td>\n",
              "      <td>sleepy face</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    word  abs_freq  wtd_freq  rel_value                     emoji_text\n",
              "0      👏         3      6483     2161.0                 clapping hands\n",
              "1      🙏         2      4322     2161.0                   folded hands\n",
              "2      🥺         1      2161     2161.0                  pleading face\n",
              "3      🍔         1      2161     2161.0                      hamburger\n",
              "4      😋         1      2161     2161.0             face savoring food\n",
              "5      🌛         1      2161     2161.0        first quarter moon face\n",
              "6      🤣         7      1946      278.0  rolling on the floor laughing\n",
              "7      😍         1       155      155.0   smiling face with heart-eyes\n",
              "8  🤦🏻‍♂️         1       136      136.0             person facepalming\n",
              "9      😪         1       135      135.0                    sleepy face"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_L20I1tJGrFF"
      },
      "source": [
        "sotu_retweets = np.load('tweets_dict.npy',allow_pickle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGohnLcbGrFJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "237508bb-54eb-409e-d6d4-edb044848b6e"
      },
      "source": [
        "def buildDataFrameFromDict(mapping):\n",
        "    df=[]\n",
        "    for f in mapping:\n",
        "        f_n = {}\n",
        "        for k,item in f.items():\n",
        "            if isinstance(item,dict):\n",
        "                for i,j in item.items():\n",
        "                    f_n[k+'-'+i] = j\n",
        "            else:\n",
        "                f_n[k] = f[k]\n",
        "        df.append(f_n)\n",
        "    \n",
        "    df = pd.DataFrame(df)\n",
        "    return df\n",
        "\n",
        "sotu = buildDataFrameFromDict(sotu_retweets)\n",
        "sotu.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>created_at</th>\n",
              "      <th>id</th>\n",
              "      <th>id_str</th>\n",
              "      <th>text</th>\n",
              "      <th>source</th>\n",
              "      <th>truncated</th>\n",
              "      <th>in_reply_to_status_id</th>\n",
              "      <th>in_reply_to_status_id_str</th>\n",
              "      <th>in_reply_to_user_id</th>\n",
              "      <th>in_reply_to_user_id_str</th>\n",
              "      <th>in_reply_to_screen_name</th>\n",
              "      <th>user-id</th>\n",
              "      <th>user-id_str</th>\n",
              "      <th>user-name</th>\n",
              "      <th>user-screen_name</th>\n",
              "      <th>user-location</th>\n",
              "      <th>user-url</th>\n",
              "      <th>user-description</th>\n",
              "      <th>user-translator_type</th>\n",
              "      <th>user-protected</th>\n",
              "      <th>user-verified</th>\n",
              "      <th>user-followers_count</th>\n",
              "      <th>user-friends_count</th>\n",
              "      <th>user-listed_count</th>\n",
              "      <th>user-favourites_count</th>\n",
              "      <th>user-statuses_count</th>\n",
              "      <th>user-created_at</th>\n",
              "      <th>user-utc_offset</th>\n",
              "      <th>user-time_zone</th>\n",
              "      <th>user-geo_enabled</th>\n",
              "      <th>user-lang</th>\n",
              "      <th>user-contributors_enabled</th>\n",
              "      <th>user-is_translator</th>\n",
              "      <th>user-profile_background_color</th>\n",
              "      <th>user-profile_background_image_url</th>\n",
              "      <th>user-profile_background_image_url_https</th>\n",
              "      <th>user-profile_background_tile</th>\n",
              "      <th>user-profile_link_color</th>\n",
              "      <th>user-profile_sidebar_border_color</th>\n",
              "      <th>user-profile_sidebar_fill_color</th>\n",
              "      <th>...</th>\n",
              "      <th>quoted_status-is_quote_status</th>\n",
              "      <th>quoted_status-quote_count</th>\n",
              "      <th>quoted_status-reply_count</th>\n",
              "      <th>quoted_status-retweet_count</th>\n",
              "      <th>quoted_status-favorite_count</th>\n",
              "      <th>quoted_status-entities</th>\n",
              "      <th>quoted_status-favorited</th>\n",
              "      <th>quoted_status-retweeted</th>\n",
              "      <th>quoted_status-filter_level</th>\n",
              "      <th>quoted_status-lang</th>\n",
              "      <th>quoted_status_permalink-url</th>\n",
              "      <th>quoted_status_permalink-expanded</th>\n",
              "      <th>quoted_status_permalink-display</th>\n",
              "      <th>quoted_status-extended_tweet</th>\n",
              "      <th>quoted_status-possibly_sensitive</th>\n",
              "      <th>display_text_range</th>\n",
              "      <th>extended_tweet-full_text</th>\n",
              "      <th>extended_tweet-display_text_range</th>\n",
              "      <th>extended_tweet-entities</th>\n",
              "      <th>possibly_sensitive</th>\n",
              "      <th>retweeted_status-extended_entities</th>\n",
              "      <th>entities-media</th>\n",
              "      <th>extended_entities-media</th>\n",
              "      <th>quoted_status-extended_entities</th>\n",
              "      <th>quoted_status-quoted_status_id</th>\n",
              "      <th>quoted_status-quoted_status_id_str</th>\n",
              "      <th>extended_tweet-extended_entities</th>\n",
              "      <th>place-id</th>\n",
              "      <th>place-url</th>\n",
              "      <th>place-place_type</th>\n",
              "      <th>place-name</th>\n",
              "      <th>place-full_name</th>\n",
              "      <th>place-country_code</th>\n",
              "      <th>place-country</th>\n",
              "      <th>place-bounding_box</th>\n",
              "      <th>place-attributes</th>\n",
              "      <th>geo-type</th>\n",
              "      <th>geo-coordinates</th>\n",
              "      <th>coordinates-type</th>\n",
              "      <th>coordinates-coordinates</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fri Oct 09 03:56:50 +0000 2020</td>\n",
              "      <td>1314414491648565248</td>\n",
              "      <td>1314414491648565248</td>\n",
              "      <td>RT @lovtyholic: ‼️‼️ayuden a reportar esta cuenta NO INTERACTUAR NO CITAR NO RESPONDER solo reporten el tweet y la c...</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" rel=\"nofollow\"&gt;Twitter for Android&lt;/a&gt;</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>3276878238</td>\n",
              "      <td>3276878238</td>\n",
              "      <td>HELLO JONGDAE 𐂅</td>\n",
              "      <td>moonsootyong</td>\n",
              "      <td>None</td>\n",
              "      <td>https://invisiblestring.carrd.co</td>\n",
              "      <td>#도경수 🐧 -108 ۵she/her۵MoonDanSe|Tyongf♥️                                                         #문태일 #태용 #정한 #เตนล์ ...</td>\n",
              "      <td>none</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>143</td>\n",
              "      <td>198</td>\n",
              "      <td>3</td>\n",
              "      <td>90612</td>\n",
              "      <td>20735</td>\n",
              "      <td>Sun Jul 12 01:50:27 +0000 2015</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>000000</td>\n",
              "      <td>http://abs.twimg.com/images/themes/theme1/bg.png</td>\n",
              "      <td>https://abs.twimg.com/images/themes/theme1/bg.png</td>\n",
              "      <td>False</td>\n",
              "      <td>F58EA8</td>\n",
              "      <td>000000</td>\n",
              "      <td>000000</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fri Oct 09 03:56:50 +0000 2020</td>\n",
              "      <td>1314414494039314432</td>\n",
              "      <td>1314414494039314432</td>\n",
              "      <td>¿qué hora es en su país cuando ven este tweet??? aquí son las 10:56</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>1230697673105133570</td>\n",
              "      <td>1230697673105133570</td>\n",
              "      <td>𝐋𝐢𝐬𝐚 ♡𝐀𝐑𝐌𝐘</td>\n",
              "      <td>Winter_bear1997</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>𝑌𝑜𝑢’𝑣𝑒 𝑠𝘩𝑜𝑤𝑛 𝑚𝑒 𝐼 𝘩𝑎𝑣𝑒 𝑟𝑒𝑎𝑠𝑜𝑛𝑠 𝐼 𝑠𝘩𝑜𝑢𝑙𝑑 𝑙𝑜𝑣𝑒 𝑚𝑦𝑠𝑒𝑙f 𝐎𝐓𝟕 𝐒𝐭𝐚𝐧 𝐹𝑎𝑛 𝑎𝑐𝑐𝑜𝑢𝑛𝑡</td>\n",
              "      <td>none</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>3503</td>\n",
              "      <td>1969</td>\n",
              "      <td>35</td>\n",
              "      <td>13784</td>\n",
              "      <td>8151</td>\n",
              "      <td>Fri Feb 21 03:36:22 +0000 2020</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>F5F8FA</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>False</td>\n",
              "      <td>1DA1F2</td>\n",
              "      <td>C0DEED</td>\n",
              "      <td>DDEEF6</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fri Oct 09 03:56:51 +0000 2020</td>\n",
              "      <td>1314414496002322432</td>\n",
              "      <td>1314414496002322432</td>\n",
              "      <td>RT @Juuandedios_p: Este tweet podra llegar a 500 comentarios con la siguiente frase:\\n\\nCANCIONES PARA MI EX\\n\\n15 c...</td>\n",
              "      <td>&lt;a href=\"http://twitter.com/download/android\" rel=\"nofollow\"&gt;Twitter for Android&lt;/a&gt;</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>1268048343202009088</td>\n",
              "      <td>1268048343202009088</td>\n",
              "      <td>𝙚𝙙𝙖🦋</td>\n",
              "      <td>edaloveskenia</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>•𝐜𝐮𝐞𝐧𝐭𝐚 𝐩𝐚𝐫𝐚 𝐯𝐨𝐭𝐨𝐬•\\n𝐜𝐮𝐞𝐧𝐭𝐚 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐚𝐥: @edapdc1\\n 𝐟𝐚𝐧 𝐚𝐜𝐜𝐨𝐮𝐧𝐭</td>\n",
              "      <td>none</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>107</td>\n",
              "      <td>136</td>\n",
              "      <td>0</td>\n",
              "      <td>80</td>\n",
              "      <td>3584</td>\n",
              "      <td>Wed Jun 03 05:14:31 +0000 2020</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>F5F8FA</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>False</td>\n",
              "      <td>1DA1F2</td>\n",
              "      <td>C0DEED</td>\n",
              "      <td>DDEEF6</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 160 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                       created_at  ...  coordinates-coordinates\n",
              "0  Fri Oct 09 03:56:50 +0000 2020  ...                      NaN\n",
              "1  Fri Oct 09 03:56:50 +0000 2020  ...                      NaN\n",
              "2  Fri Oct 09 03:56:51 +0000 2020  ...                      NaN\n",
              "\n",
              "[3 rows x 160 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atLOTuX4GrFO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e0b0f80f-ca77-4268-a05c-b29669cb05aa"
      },
      "source": [
        "pprint(sotu.columns.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "array(['created_at', 'id', 'id_str', 'text', 'source', 'truncated',\n",
            "       'in_reply_to_status_id', 'in_reply_to_status_id_str',\n",
            "       'in_reply_to_user_id', 'in_reply_to_user_id_str',\n",
            "       'in_reply_to_screen_name', 'user-id', 'user-id_str', 'user-name',\n",
            "       'user-screen_name', 'user-location', 'user-url',\n",
            "       'user-description', 'user-translator_type', 'user-protected',\n",
            "       'user-verified', 'user-followers_count', 'user-friends_count',\n",
            "       'user-listed_count', 'user-favourites_count',\n",
            "       'user-statuses_count', 'user-created_at', 'user-utc_offset',\n",
            "       'user-time_zone', 'user-geo_enabled', 'user-lang',\n",
            "       'user-contributors_enabled', 'user-is_translator',\n",
            "       'user-profile_background_color',\n",
            "       'user-profile_background_image_url',\n",
            "       'user-profile_background_image_url_https',\n",
            "       'user-profile_background_tile', 'user-profile_link_color',\n",
            "       'user-profile_sidebar_border_color',\n",
            "       'user-profile_sidebar_fill_color', 'user-profile_text_color',\n",
            "       'user-profile_use_background_image', 'user-profile_image_url',\n",
            "       'user-profile_image_url_https', 'user-profile_banner_url',\n",
            "       'user-default_profile', 'user-default_profile_image',\n",
            "       'user-following', 'user-follow_request_sent', 'user-notifications',\n",
            "       'geo', 'coordinates', 'place', 'contributors',\n",
            "       'retweeted_status-created_at', 'retweeted_status-id',\n",
            "       'retweeted_status-id_str', 'retweeted_status-text',\n",
            "       'retweeted_status-display_text_range', 'retweeted_status-source',\n",
            "       'retweeted_status-truncated',\n",
            "       'retweeted_status-in_reply_to_status_id',\n",
            "       'retweeted_status-in_reply_to_status_id_str',\n",
            "       'retweeted_status-in_reply_to_user_id',\n",
            "       'retweeted_status-in_reply_to_user_id_str',\n",
            "       'retweeted_status-in_reply_to_screen_name',\n",
            "       'retweeted_status-user', 'retweeted_status-geo',\n",
            "       'retweeted_status-coordinates', 'retweeted_status-place',\n",
            "       'retweeted_status-contributors',\n",
            "       'retweeted_status-is_quote_status',\n",
            "       'retweeted_status-extended_tweet', 'retweeted_status-quote_count',\n",
            "       'retweeted_status-reply_count', 'retweeted_status-retweet_count',\n",
            "       'retweeted_status-favorite_count', 'retweeted_status-entities',\n",
            "       'retweeted_status-favorited', 'retweeted_status-retweeted',\n",
            "       'retweeted_status-possibly_sensitive',\n",
            "       'retweeted_status-filter_level', 'retweeted_status-lang',\n",
            "       'is_quote_status', 'quote_count', 'reply_count', 'retweet_count',\n",
            "       'favorite_count', 'entities-hashtags', 'entities-urls',\n",
            "       'entities-user_mentions', 'entities-symbols', 'favorited',\n",
            "       'retweeted', 'filter_level', 'lang', 'timestamp_ms',\n",
            "       'retweeted_status-quoted_status_id',\n",
            "       'retweeted_status-quoted_status_id_str',\n",
            "       'retweeted_status-quoted_status',\n",
            "       'retweeted_status-quoted_status_permalink', 'quoted_status_id',\n",
            "       'quoted_status_id_str', 'quoted_status-created_at',\n",
            "       'quoted_status-id', 'quoted_status-id_str', 'quoted_status-text',\n",
            "       'quoted_status-display_text_range', 'quoted_status-source',\n",
            "       'quoted_status-truncated', 'quoted_status-in_reply_to_status_id',\n",
            "       'quoted_status-in_reply_to_status_id_str',\n",
            "       'quoted_status-in_reply_to_user_id',\n",
            "       'quoted_status-in_reply_to_user_id_str',\n",
            "       'quoted_status-in_reply_to_screen_name', 'quoted_status-user',\n",
            "       'quoted_status-geo', 'quoted_status-coordinates',\n",
            "       'quoted_status-place', 'quoted_status-contributors',\n",
            "       'quoted_status-is_quote_status', 'quoted_status-quote_count',\n",
            "       'quoted_status-reply_count', 'quoted_status-retweet_count',\n",
            "       'quoted_status-favorite_count', 'quoted_status-entities',\n",
            "       'quoted_status-favorited', 'quoted_status-retweeted',\n",
            "       'quoted_status-filter_level', 'quoted_status-lang',\n",
            "       'quoted_status_permalink-url', 'quoted_status_permalink-expanded',\n",
            "       'quoted_status_permalink-display', 'quoted_status-extended_tweet',\n",
            "       'quoted_status-possibly_sensitive', 'display_text_range',\n",
            "       'extended_tweet-full_text', 'extended_tweet-display_text_range',\n",
            "       'extended_tweet-entities', 'possibly_sensitive',\n",
            "       'retweeted_status-extended_entities', 'entities-media',\n",
            "       'extended_entities-media', 'quoted_status-extended_entities',\n",
            "       'quoted_status-quoted_status_id',\n",
            "       'quoted_status-quoted_status_id_str',\n",
            "       'extended_tweet-extended_entities', 'place-id', 'place-url',\n",
            "       'place-place_type', 'place-name', 'place-full_name',\n",
            "       'place-country_code', 'place-country', 'place-bounding_box',\n",
            "       'place-attributes', 'geo-type', 'geo-coordinates',\n",
            "       'coordinates-type', 'coordinates-coordinates',\n",
            "       'retweeted_status-user-screen_name'], dtype=object)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2vISmP3GrFQ"
      },
      "source": [
        "data = sotu.sample(10)['user-screen_name']\n",
        "sotu['retweeted_status-user-screen_name'] = np.random.choice(data,len(sotu))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSEctOb2GrFS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "628dff3e-74ff-47d2-8f53-df6c24e1ebbe"
      },
      "source": [
        "sotu[['user-screen_name','retweeted_status-user-screen_name']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user-screen_name</th>\n",
              "      <th>retweeted_status-user-screen_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>moonsootyong</td>\n",
              "      <td>whotamelonsugar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Winter_bear1997</td>\n",
              "      <td>YoceOs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>edaloveskenia</td>\n",
              "      <td>Constanpr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LeeBer37753434</td>\n",
              "      <td>leonXD__</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>FelipeCucalon</td>\n",
              "      <td>Michi_Ca_Pin</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  user-screen_name retweeted_status-user-screen_name\n",
              "0     moonsootyong                   whotamelonsugar\n",
              "1  Winter_bear1997                            YoceOs\n",
              "2    edaloveskenia                         Constanpr\n",
              "3   LeeBer37753434                          leonXD__\n",
              "4    FelipeCucalon                      Michi_Ca_Pin"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRs2rtruGrFW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "125bab97-5989-4687-d3b8-7a4e39424579"
      },
      "source": [
        "G_rt = nx.from_pandas_edgelist(\n",
        "    sotu,\n",
        "    source = 'user-screen_name', \n",
        "    target = 'retweeted_status-user-screen_name',\n",
        "    create_using = nx.DiGraph())\n",
        "    \n",
        "print('Nodes in RT network:', len(G_rt.nodes()))\n",
        "print('Edges in RT network:', len(G_rt.edges()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nodes in RT network: 589\n",
            "Edges in RT network: 609\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_rZumXjGrFX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d9a80ab8-0fbb-4738-eedb-2137ab3bfaf1"
      },
      "source": [
        "G_reply = nx.from_pandas_edgelist(\n",
        "    sotu,\n",
        "    source = 'user-screen_name', \n",
        "    target = 'in_reply_to_screen_name',\n",
        "    create_using = nx.DiGraph())\n",
        "    \n",
        "print('Nodes in reply network:', len(G_reply.nodes()))\n",
        "\n",
        "print('Edges in reply network:', len(G_reply.edges()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nodes in reply network: 749\n",
            "Edges in reply network: 598\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkk0qnxRGrFZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "0fd3c035-22e6-4782-9b5e-40e9ee838f90"
      },
      "source": [
        "pos = nx.random_layout(G_rt)\n",
        "\n",
        "sizes = [x[1] for x in G_rt.degree()]\n",
        "\n",
        "nx.draw_networkx(G_rt, pos, \n",
        "    with_labels = False, \n",
        "    node_size = sizes,\n",
        "    width = 0.1, alpha = 0.7,\n",
        "    arrowsize = 2, linewidths = 0)\n",
        "\n",
        "plt.savefig('snapshot/lima_tweets_influencing_graph.png')\n",
        "plt.axis('off'); plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2MrzgNFfGrFb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "9b8c7c7e-79b2-406b-d59c-d4a317baa626"
      },
      "source": [
        "pos = nx.random_layout(G_reply)\n",
        "sizes = [x[1] for x in G_reply.degree()]\n",
        "\n",
        "nx.draw_networkx(G_reply, pos, \n",
        "    with_labels = False, \n",
        "    node_size = sizes,\n",
        "    width = 0.1, alpha = 0.7,\n",
        "    arrowsize = 2, linewidths = 0)\n",
        "\n",
        "plt.axis('off'); plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyJEPk8tGrFe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "ebb15d1e-95d2-4ed2-ffb6-1fb256499ee4"
      },
      "source": [
        "column_names = ['screen_name', 'degree_centrality']\n",
        "rt_centrality = nx.in_degree_centrality(G_rt)\n",
        "\n",
        "reply_centrality = nx.in_degree_centrality(G_reply)\n",
        "\n",
        "rt = pd.DataFrame(list(rt_centrality.items()), columns = column_names)\n",
        "reply = pd.DataFrame(list(reply_centrality.items()), columns = column_names)\n",
        "\n",
        "display(rt.sort_values('degree_centrality', ascending = False).head())\n",
        "\n",
        "display(reply.sort_values('degree_centrality', ascending = False).head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>screen_name</th>\n",
              "      <th>degree_centrality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>corzin443</td>\n",
              "      <td>0.134354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>leonXD__</td>\n",
              "      <td>0.127551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>whotamelonsugar</td>\n",
              "      <td>0.119048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Constanpr</td>\n",
              "      <td>0.103741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Danna_ArdilaO</td>\n",
              "      <td>0.102041</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        screen_name  degree_centrality\n",
              "14        corzin443           0.134354\n",
              "7          leonXD__           0.127551\n",
              "1   whotamelonsugar           0.119048\n",
              "5         Constanpr           0.103741\n",
              "34    Danna_ArdilaO           0.102041"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>screen_name</th>\n",
              "      <th>degree_centrality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>0.570856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>leoni70803995</td>\n",
              "      <td>0.002674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>lovely_min_k</td>\n",
              "      <td>0.002674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>jmvalerapiedras</td>\n",
              "      <td>0.002674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>jimenaajimenezr</td>\n",
              "      <td>0.002674</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         screen_name  degree_centrality\n",
              "1               None           0.570856\n",
              "111    leoni70803995           0.002674\n",
              "369     lovely_min_k           0.002674\n",
              "546  jmvalerapiedras           0.002674\n",
              "62   jimenaajimenezr           0.002674"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fT6t9GOGrFf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "cbb824d4-3f20-485e-f560-686e43584b40"
      },
      "source": [
        "column_names = ['screen_name', 'betweenness_centrality']\n",
        "# Generate betweenness centrality for retweets \n",
        "rt_centrality = nx.betweenness_centrality(G_rt)\n",
        "\n",
        "# Generate betweenness centrality for replies \n",
        "reply_centrality = nx.betweenness_centrality(G_reply)\n",
        "\n",
        "# Store centralities in data frames\n",
        "rt = pd.DataFrame(list(rt_centrality.items()), columns = column_names)\n",
        "reply = pd.DataFrame(list(reply_centrality.items()), columns = column_names)\n",
        "\n",
        "# Print first five results in descending order of centrality\n",
        "display(rt.sort_values('betweenness_centrality', ascending = False).head())\n",
        "\n",
        "# Print first five results in descending order of centrality\n",
        "display(reply.sort_values('betweenness_centrality', ascending = False).head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>screen_name</th>\n",
              "      <th>betweenness_centrality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>corzin443</td>\n",
              "      <td>0.004016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>kmiilinn</td>\n",
              "      <td>0.002161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Michi_Ca_Pin</td>\n",
              "      <td>0.001792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>whotamelonsugar</td>\n",
              "      <td>0.001751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Crackalaka2</td>\n",
              "      <td>0.001686</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        screen_name  betweenness_centrality\n",
              "14        corzin443                0.004016\n",
              "51         kmiilinn                0.002161\n",
              "9      Michi_Ca_Pin                0.001792\n",
              "1   whotamelonsugar                0.001751\n",
              "28      Crackalaka2                0.001686"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>screen_name</th>\n",
              "      <th>betweenness_centrality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>rosamt5</td>\n",
              "      <td>0.000002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>509</th>\n",
              "      <td>stigmxjk</td>\n",
              "      <td>0.000002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>LuSKabeche</td>\n",
              "      <td>0.000002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>moonsootyong</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>Castelopio</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      screen_name  betweenness_centrality\n",
              "261       rosamt5                0.000002\n",
              "509      stigmxjk                0.000002\n",
              "76     LuSKabeche                0.000002\n",
              "0    moonsootyong                0.000000\n",
              "502    Castelopio                0.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kq-0BfOAGrFh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f8dc3f76-b6a1-4078-c300-9dd914436054"
      },
      "source": [
        "column_names = ['screen_name', 'degree']\n",
        "\n",
        "degree_rt = pd.DataFrame(list(G_rt.in_degree()), columns = column_names)\n",
        "degree_reply = pd.DataFrame(list(G_reply.in_degree()), columns = column_names)\n",
        "\n",
        "ratio = degree_rt.merge(degree_reply, on = 'screen_name', suffixes = ('_rt', '_reply'))\n",
        "\n",
        "ratio['ratio'] = ratio['degree_reply'] / ratio['degree_rt']\n",
        "\n",
        "ratio = ratio[ratio['degree_rt'] >= 5]\n",
        "\n",
        "display(ratio.sort_values('ratio', ascending = False).head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>screen_name</th>\n",
              "      <th>degree_rt</th>\n",
              "      <th>degree_reply</th>\n",
              "      <th>ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>corzin443</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "      <td>0.012658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>whotamelonsugar</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>YoceOs</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Constanpr</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>leonXD__</td>\n",
              "      <td>75</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        screen_name  degree_rt  degree_reply     ratio\n",
              "14        corzin443         79             1  0.012658\n",
              "1   whotamelonsugar         70             0  0.000000\n",
              "3            YoceOs         51             0  0.000000\n",
              "5         Constanpr         61             0  0.000000\n",
              "7          leonXD__         75             0  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQXFda9k8ihF"
      },
      "source": [
        "%%capture\n",
        "!jupyter nbconvert --to html Live_tweeting.ipynb --output index.html\n",
        "with open('index.html', 'r') as file:\n",
        "    content = file.read()\n",
        "content = content.replace(\"div.input_area {\",\"div.input_area {\\n\\tdisplay: none;\")    \n",
        "content = content.replace(\".prompt {\",\".prompt {\\n\\tdisplay: none;\")\n",
        "f = open('index.html', 'w')\n",
        "f.write(content)\n",
        "f.close()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}