Customer Review Data Analysis

I have a collection of customer reviews that I need turned into clear, actionable insight. This is strictly customer–level data and the single goal is to analyse the feedback itself—no financial or operational metrics are involved.

The raw reviews sit in CSV files with text, rating, product ID, and time stamps. I want the data cleaned, explored, and mined for sentiment and recurring themes so my product and marketing teams can see what drives satisfaction or frustration.

Deliverables
• A cleaned, well-documented dataset ready for ad-hoc queries
• A fully reproducible analysis notebook (Python is preferred; R is acceptable) showing all steps from import to visualisation
• A concise slide or dashboard pack highlighting sentiment distribution, top themes, and notable anomalies
• Brief written recommendations that tie the findings back to product or service improvements

Acceptance criteria: the notebook runs end-to-end on my machine, visual totals match the code output, and insights directly address the customer-feedback objectives.

Preferred tools include Python, pandas, scikit-learn, NLTK or spaCy for text mining, plus Tableau or Power BI for visuals, but I’m open to equivalent alternatives as long as everything is clearly commented and easy to rerun.

This is a focused analysis project; once we agree on scope and timeline I’ll share sample data so you can confirm approach before moving ahead.