Sentiment analysis dashboard for Twitter hashtags
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.
Model Setup:
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
.)
Configuration:
server/main/config.py
.Starting the App:
docker-compose up --build
in the terminal from the root directory.Run the Kaggle Notebook for CNN Sentiment Classification.
sentiment140.csv
in the root folder.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.)