Govind-Sankar/IMDb-Sentiment-Analyzer

IMDb Sentiment Analyzer

A machine learning + deep learning project for sentiment analysis on the IMDb movie reviews dataset. This project compares classical ML models (Logistic Regression, SVM, Random Forest) with a fine-tuned DistilBERT transformer model. The web interface is built with Streamlit.

Website Link:

Click Here

Features

• Train and use classical ML models (LogReg, SVM, RF).
• Fine-tuned DistilBERT model for state-of-the-art performance.
• Interactive Streamlit web app for real-time predictions.
• Model comparison dashboard with accuracy, precision, recall, and F1-score.

Screenshots

Repository Structure

IMDb-Sentiment-Analyzer/
│── app.py                 # Streamlit app
│── classical_models.py    # Script to train classical models
│── requirements.txt       # Dependencies
│── README.md              # Project documentation
│── IMDB Dataset.csv       # Dataset
│
├── models/
│   ├── logreg_model.pkl
│   ├── svm_model.pkl
│   ├── rf_model.pkl
│   └── vectorizer.pkl
│
├── sentiment_model/       # Fine-tuned DistilBERT 
│   ├── config.json
│   ├── pytorch_model.bin
│   ├── vocab.txt
│   └── ...
|
├── images/                # Screenshots
│   ├── Screenshot_1.png
│   ├── Screenshot_2.png
│   └── Screenshot_3.png
│
└── notebooks/
    └── dilbert.ipynb      # Jupyter notebook for BERT fine-tuning

Models & Results

Metric	Logistic Regression	SVM	Random Forest
Accuracy	90.76%	91.79%	97.12%
Precision	90.01%	91.31%	97.32%
Recall	91.70%	92.36%	96.91%
F1-Score	90.85%	91.84%	97.11%

Installation

1. Clone the repository:

   git lfs install
   git clone https://huggingface.co/Govind-Sankar/IMDb-Sentiment-Analyzer
   cd IMDb-Sentiment-Analyzer
   

2. Install dependencies:

   pip install -r requirements.txt
   

Usage

1. Run the Streamlit app:

   streamlit run app.py
   

2. Enter a review and see the sentiment prediction.

3. Compare different models on metrics and visualizations.

Training

• To retrain classical ML models:

  python classical_models.py
  

  This will regenerate .pkl model files in /models/.

• To retrain DistilBERT: Open notebooks/dilbert.ipynb in Jupyter/Colab and run all cells.

License

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

MIT License

Copyright (c) 2025 Govind Sankar

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.