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
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.
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.
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.
