Uber Customer Sentiment Analysis & Product Insights

This project focused on analyzing large-scale user feedback to surface actionable product insights. I worked with over 12,000 Uber app reviews, transforming unstructured text data into structured themes to better understand user sentiment and recurring pain points across the app experience.

I categorized reviews by feature area (e.g., payments, app performance, driver experience, safety, wait time) and sentiment, then visualized patterns to identify which issues were most strongly associated with negative ratings. The analysis highlighted that payment transparency and app reliability were the most common drivers of dissatisfaction, particularly among low-rated reviews.

Key components of the analysis included:

• Aggregating and cleaning raw review data

• Classifying feedback into product-relevant categories

• Visualizing sentiment distribution and complaint drivers using dashboards

The findings were synthesized into prioritized product recommendations, focusing on improving billing clarity, refund flows, and booking stability. This project demonstrates my ability to translate qualitative user feedback into structured insights that can directly inform product and UX decisions.