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Sentiment Analyzer

The Sentiment Analyzer is a Flask based web application that allows users to analyze the sentiment of text, comments, or reviews. It uses natural language processing techniques to classify the sentiment as positive, negative, or neutral. The model has achieved an accuracy of 93.05% using the Gradient Boosting Classifier.

Screenshots

screenshot 1 Screenshot 2
Home Page Result Page

How to Run

  1. Clone the repository:

    git clone https://github.com/yourusername/sentiment-analyzer.git

  2. Create a virtual environment and activate it:

    python -m venv

  3. Install the dependencies:

    pip install -r requirements.txt

  4. Run the application:

    run app.py

  5. Open your web browser and visit:

    http://127.0.0.1:5000/

Dataset Availability

The dataset used for training the model can be downloaded from Kaggle.

License

This project is licensed under the MIT License. See the LICENSE file for more details.