Movie-Review-Sentiment-Analysis

Overview

• The goal of this project is to develop a machine learning model that uses natural language processing (NLP) techniques to automatically categorize movie reviews into two sentiment categories: positive and negative.
• This project leverages various machine learning algorithms to classify sentiment, such as Naive Bayes, Logistic Regression and Random Forest, and provides a user-friendly interface through a Streamlit web app.

Features

• Movie Review Sentiment Classification: Automatically categorize movie reviews into positive or negative sentiment using NLP techniques.
• Machine Learning Models: Implements Logistic Regression and Random Forest models for classification.
• Real-time Analysis: The Streamlit app allows users to input movie reviews and get instant sentiment classification results.
• Customizable UI: The app includes a personalized Streamlit theme for better user experience.

Installation

Follow these steps to set up and run the project locally:

1. Clone the repository

git clone https://github.com/yourusername/Movie-Review-Sentiment-Analysis.git
cd Movie-Review-Sentiment-Analysis

2. Create a virtual environment and activate it (optional but recommended)

python -m venv env
source env/bin/activate   # For Linux/macOS
env\Scripts\activate      # For Windows

3. Install dependencies

pip install -r requirements.txt

4.Run the Jupyter Notebook

jupyter notebook notebooks/sentiment_analysis.ipynb

5.Run the Streamlit app

streamlit run sentiment_analysis.py