Twitter Sentiment Analysis

Warning

The Wikipedia API might not work all the time depending on the entered search term.

This is a web app which can be used to analyze users' sentiments across Twitter hashtags. Its created using React and Django and uses an LSTM model trained on the Kaggle Sentiment140 dataset and served as a REST API to the ReactJS frontend.

The server pulls tweets using tweepy and performs inference using Keras. It also pulls data from the Wikipedia API based the hashtag chosen to display a short description. As part of the analysis, I also added few examples of the tweets and their predicted sentiments. A kernel for another sentiment classification using a CNN + 1D pooling can be found here

How to Use

Running the application

Download the trained model and put into the server/main folder
(Note: This is the CNN model. f you want use the LSTM model, you'll need to follow the training steps below and put the saved model in server/main. Also, don't forget to change the loaded model name in server/main/init.py )
Run python -m venv venv in the terminal from the server folder to create a Python virtual environment
(Note: Ensure that you have Python version 3.7 or higher)
Run venv/scripts/activate and pip install -r requirements.txt while still being in server folder to switch to virtual env & install all the required packages
Start the Flask server by running python app.py
Open http://localhost:8000 in your browser to access the app

Updating front-end app

Run npm install in client folder to download all the dependent noad modules
Make the changes in client/src folder as required
Check the front-end related changes by running npm start & open http://localhost:5000 in your browser
Run npm run build after all the changes are made to create optimized production build

Training the model