Welcome to the Sentiment Analysis project, where we explore the fascinating world of sentiment prediction using cutting-edge deep learning models and deploy them seamlessly using Gradio. Sentiment analysis, also known as opinion mining, is the process of determining the emotional tone or sentiment expressed in text data, whether it's positive, negative, or neutral.
In this project, we leverage the power of state-of-the-art deep learning models to analyze and understand sentiment in text. Sentiment analysis has a wide range of applications, from monitoring social media sentiment about products or brands to understanding customer reviews and feedback. By automating sentiment analysis, we can gain valuable insights from large volumes of text data quickly and accurately.
Below is a preview showcasing the app's appearance.
Follow this tab to access the Deployed app on HuggingFace
Our sentiment analysis project offers the following key features:
Deep Learning Models: We harness the capabilities of advanced deep learning models designed for natural language processing (NLP). These models have been pretrained on vast text corpora and can efficiently capture semantic meaning and sentiment in text data.
Data Preparation: We emphasize the importance of data preparation, including text preprocessing, tokenization, and data splitting. These steps are crucial to ensure that the data is in a suitable format for training and evaluation.
Model Training: We provide resources and scripts to train sentiment analysis models on your own dataset. You can fine-tune pretrained models to adapt them to your specific task and domain.
Evaluation Metrics: We evaluate model performance using various metrics, such as accuracy, F1 score, and more, to measure how effectively the models predict sentiment in text.
Deployment with Gradio : Firstly, The fine-tuned model, along with its associated files, is uploaded to Hugging Face for accessibility. Our project showcases the deployment of sentiment analysis models using Gradio, a user-friendly library for creating customizable UI interfaces for machine learning models. This allows you to interactively analyze sentiment in real-time with ease and the resulting application is hosted on Hugging Face.
Docker Containerization: Docker was leveraged to containerize the application, ensuring a streamlined deployment process and enhanced scalability.
To get started with sentiment analysis using deep learning models and Gradio, follow our comprehensive documentation and examples. We provide step-by-step instructions, code snippets, and resources to help you build, train, and deploy sentiment analysis solutions tailored to your needs. S
To run this sentiment analysis project, you'll need to set up a Python environment and install the required dependencies. We recommend using a virtual environment for managing project-specific packages. If you're using Google Colab, you can still set up a virtual environment for your project.
**Create a Virtual Environment:**Navigate to your project directory and create a virtual environment.
Installing Dependencies After setting up your virtual environment or if you prefer to work in your local Python environment, you can install the required dependencies from the requirements.txt file.
This will install the necessary packages, including Pandas, Datasets, Scikit-Learn, Transformers, and other libraries required for the project.
Clone the Repository:
Clone this repository to your local machine or Google Colab environment:
git clone <repository-url>
cd <project-directory>
This project was developed during the Azubi Africa Data Science Training. Below is the details of the initial collaborators of this project with respective articles covering the process of the project and their individual github profiles.
| Name | Article | Github |
|---|---|---|
Here are a few recommended resources to help you gain a solid understanding of the frameworks used in the project:
Get to know about Hugging Face
This project is open-source and available under the MIT License. Feel free to use, modify, and distribute it in accordance with the license terms.
We extend our heartfelt appreciation to Azubi Africa for offering us the opportunity to engage in this educational endeavor and for welcoming us as participants in this program. Additionally, we'd like to acknowledge the invaluable contributions of the open-source community and the dedicated developers of deep learning frameworks, NLP libraries, and Gradio, whose efforts have paved the way for projects of this nature to thrive. Your support has been instrumental in our journey.
Thank you for joining us on this exciting journey into the world of sentiment analysis with deep learning models and Gradio!