This Flask-based web app analyzes tweets' sentiment using a pre-trained machine learning model. The application provides a simple user interface to input tweets and get sentiment predictions.
Check out the live deployment of the application on Heroku: Sentiment Analysis Web App
The project consists of the following main files and directories:
static/style.css: CSS file for styling the frontend.templates/index.html: HTML template for the web interface.app.py: The main Flask application containing the sentiment analysis logic.lr_model.pkl: Pre-trained machine learning model (Linear Regression) for sentiment analysis.Procfile: Configuration file for Heroku deployment.requirements.txt: List of required Python packages for the project.test_app.py: Unit tests for the application.tfidf.pickle: Pickled TF-IDF vectorizer used for text preprocessing.
Follow these steps to run the application on your local machine:
- Clone the repository.
- Install dependencies:
pip install -r requirements.txt
- Run the application:
python app.py
- Access the application in your web browser at http://127.0.0.1:5000/
- Enter a tweet in the input field and submit it.
- The application will process the input and display the sentiment classification result as either 'Positive' or 'Bad Buzz !!!'.
You can customize the application in the following ways:
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Model and Vectorizer: If needed, replace the pre-trained machine learning model (lr_model.pkl) and vectorizer (tfidf.pickle) with your own trained models.
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Emojis and Preprocessing: Modify the EMOJIS dictionary and the related functions in app.py to change emoji descriptions and preprocessing steps.
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Frontend: Modify the templates/index.html file to adjust the appearance and layout of the application.
Run unit tests to ensure the application's functionality:
bash pytest test_app.py
This application is deployed on Heroku using the provided Procfile and requirements.txt.
This project is licensed under the MIT License. Feel free to fork the repository and adapt it to your needs.