Amazon Reviews Sentiment Analysis & Genre-Based Recommender System

This project involves analyzing a large Kaggle dataset of Amazon reviews, developing a sentiment analysis model, and creating a genre-based book recommender system.

Project Overview

1. Sentiment Analysis:

   • Utilized the VADER (Valence Aware Dictionary and sEntiment Reasoner) tool to perform sentiment analysis on Amazon reviews.
   • The dataset consists of 3 million rows, providing a large-scale basis for sentiment classification.

2. Genre-Based Recommender:

   • Developed a book recommendation system based on genres, using cosine similarity to recommend books by taking into account both genre and sentiment ratings from the reviews.

3. Efficient Handling of Large Data:

   • The project effectively handled the large dataset, combining sentiment insights and genre preferences to provide personalized book recommendations.

Tools & Technologies

• Python: Primary programming language.
• VADER: For sentiment analysis.
• Cosine Similarity: Used for genre-based recommendations.
• Pandas, NumPy: Data manipulation and analysis.
• Matplotlib, Seaborn: For data visualization.