This web application allows users to analyze sentiments across Twitter hashtags/terms. It's built using React and Django, leveraging an LSTM model trained on the Kaggle Sentiment140 dataset. The model is served as a REST API to the ReactJS frontend.

Features

• Sentiment Analysis: Using an LSTM model to analyze sentiments on Twitter.
• Integration with Tweepy: For fetching real-time tweets.
• Wikipedia API: To provide context about the hashtags.
• Visual Examples: Displaying tweets with their predicted sentiments.
• Additional Resource: A kernel for sentiment classification using CNN + 1D pooling is available here.

Getting Started

Prerequisites

• Docker installed on your system.
• Twitter API Bearer Token from Twitter Developer Portal.

Running the Application

1. Model Setup:

   • Download the trained CNN model and place it in the server/main folder. (Note: To use the LSTM model, follow the training steps below and save the model in the server/main folder. Modify the loaded model name in server/main/init.py.)

2. Configuration:

   • Add your Twitter API Bearer Token to server/main/config.py.

3. Starting the App:

   • Run docker-compose up --build in the terminal from the root directory.
   • Access the app via http://localhost:3000.

Training the Model

CNN Model

Run the Kaggle Notebook for CNN Sentiment Classification.

LSTM Model

• Download the Kaggle Sentiment140 dataset and place it as sentiment140.csv in the root folder.
• Execute the code in Twitter Sentiment Analysis.ipynb.

(Note: The LSTM model requires more time to train due to its sequential nature. It offers performance similar to the CNN model, but a GPU is recommended for faster processing.)