Google Review Analysis for User Insights

Restaurant clients had thousands of Google reviews but no practical way to evaluate them at scale. Manually reading the feedback was slow and inconsistent, making it difficult to identify recurring complaints, understand customer satisfaction, compare locations, or determine which operational changes would have the greatest impact.

I developed a Python-based review analysis workflow that cleaned and standardized 10,000 Google reviews before evaluating sentiment, recurring topics, rating patterns, and frequently mentioned customer experiences. The results were organized into clear visual reports using Matplotlib and Plotly, allowing clients to explore positive and negative feedback, identify emerging trends, and understand the factors influencing customer ratings. Each report translated the findings into focused, actionable recommendations tailored to the restaurant's priorities and service goals.