Movie Review Sentiment Analysis

This repository demonstrates movie review sentiment classification using Natural Language Processing (NLP). It employs Random Forest, Multinomial Naive Bayes, and a Pipeline for efficient workflows.

Features

• Analyze IMDB movie reviews to classify sentiments as positive or negative.
• Preprocess review text with CountVectorizer.
• Train and evaluate Random Forest and Multinomial Naive Bayes models.
• Use a Pipeline for streamlined operations.
• Evaluate models using precision, recall, and F1-score.

Requirements

The following Python libraries are required to run the notebook:

• pandas
• numpy
• scikit-learn

Dataset

The dataset is sourced from IMDB Movie Dataset on Kaggle. Ensure the file IMDB_Dataset.csv is placed in the Dataset directory.

Clone the repository:

git clone https://github.com/umair801/Movie_Sentiment_Analysis.git