AI-Powered Customer Review Analysis App

The Problem

Analyzing hundreds of customer reviews for a key product line was a manual, repetitive, and error-prone task. My team needed an efficient way to identify recurring customer complaints, product strengths, sentiment patterns, and competitive insights — without spending hours reading every single review.

The Solution

I designed and built an interactive web application using Python and Streamlit. The app features:

• Sentiment Analysis Dashboard — Visualize positive, negative, and neutral customer sentiment at a glance
• Trend Identification — Automatically detects the most common complaints, praise points, and feature requests
• AI Assistant Integration — An embedded AI chatbot powered by LLM APIs that provides detailed reports on product strengths, weaknesses, and actionable improvement areas
• Data Upload — Upload review datasets in CSV format for instant analysis
• Visual Charts — Interactive charts and graphs built with Pandas and Streamlit's native charting

The Impact

This tool transformed what used to be hours of manual review reading into an automated, insightful process. Product teams can now make data-driven decisions in minutes instead of days, identifying the most critical areas for product improvement with confidence.