Sentiment Analysis Website

This project implements a simple web application for sentiment analysis using a machine learning model trained on IMDb movie reviews dataset. Users can input text, and the application predicts whether the sentiment of the text is positive or negative..

Features

• Input text area for users to enter text for sentiment analysis.
• Analyze Sentiment button triggers the sentiment analysis process.
• Display of predicted sentiment result.

Screenshots

1. Home Page

2. Sentiment Analysis Result

   

Technologies Used

• Backend: Python (Flask)
• Frontend: HTML, CSS, JavaScript
• Machine Learning Model: Logistic Regression (scikit-learn)
• Text Preprocessing: NLTK (Natural Language Toolkit)
• Serialization: Pickle

Setup Instructions

1. Clone the repository:

   git clone https://github.com/your_username/sentiment-analysis-website.git

2. Install dependencies:

   pip install -r requirements.txt

3. Train the machine learning model and save it as a pickle file:

   python train_model.py

4. Run the Flask server:

   python sentiment_analysis_backend.py

Directory Structure

sentiment-analysis-website/
│
├── sentiment_analysis_backend.py  # Flask backend for sentiment analysis
├── sentiment_model.py              # Pre-trained machine learning model
├── index.html                      # HTML file for frontend
├── imdb_reviews.csv                # IMDb movie reviews dataset
└── README.md                       # Project README file

License

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

Acknowledgements

• IMDb for providing the movie reviews dataset.
• OpenAI for providing guidance on machine learning techniques.
• Flask, NLTK, scikit-learn, and other open-source libraries used in this project.