Amazon Product Review Sentiment Analysis

Overview

Developed an interactive web application to analyze customer sentiment from Amazon product reviews. The tool processes large volumes of text data, classifies sentiment (positive, negative, neutral), and presents insights through an intuitive dashboard built with Streamlit.

The Challenge

• Manually reading thousands of reviews to understand customer perception is impossible
• Traditional rating averages don't capture nuanced customer opinions
• Need to identify specific pain points and positive aspects mentioned in reviews
• Require real-time analysis capability for product teams

Solution

• Implemented NLTK for text preprocessing (tokenization, stopword removal, lemmatization)
• Built sentiment classification model using NLP techniques
• Created interactive Streamlit dashboard with file upload capability
• Developed visualization of sentiment distribution and trends
• Added word cloud generation for most common positive/negative terms
• Enabled filtering by product category and rating

Technologies Used

Results & Impact

The application successfully classified sentiment across thousands of reviews, providing actionable insights into customer perception. Product teams could quickly identify common complaints and praised features, enabling data-driven product improvements.