Sentiment Analysis of User Reviews
Analyzing user reviews with NLP to classify sentiment, extract insights, and enhance product experiences.
Problem Statement:
This project aims to analyze user reviews of ChatGPT from the iOS store to classify sentiment, extract product-related feedback, and identify trends in user satisfaction. The findings will provide actionable recommendations for improving usability, functionality, and customer satisfaction.
Summary
This project leverages natural language processing (NLP) techniques to analyze user feedback for ChatGPT on the iOS store. Sentiment analysis tools like VADER and clustering algorithms like K-Means were applied to uncover patterns in user sentiment and feature preferences. Challenges in clustering led to considerations of supervised models like SVM for improved classification. Insights from this analysis offer actionable recommendations for enhancing product features and customer satisfaction while highlighting opportunities for personalized marketing and product development.