Identify Key Customer Complaints & Ideas

I have a single spreadsheet packed with open-ended customer comments from recent surveys. What I need is a clear, data-backed picture of our customers’ main complaints and their most common suggestions for improvement.

You will start with the raw text (no ratings, just written feedback) and use text-analysis techniques—keyword extraction, topic modelling, sentiment scoring, or any other NLP method you trust in Python, R, or even advanced Excel/Power Query—to surface the dominant themes. I’m interested in facts, not opinions: show me which issues come up most frequently, how strongly they are worded, and where suggestions cluster.

Deliverables
• A concise report (PDF or slide deck) summarising the top complaint categories and suggestion themes, backed by counts, percentages, and example quotes.
• An annotated spreadsheet or separate CSV with each comment mapped to its assigned theme(s) and sentiment score.
• Optional but welcome: visual aids such as bar charts, word clouds, or a simple dashboard (Tableau, Power BI, or similar) that I can share internally.

Acceptance criteria
• Every comment in the file is tagged with at least one complaint or suggestion theme.
• The final report highlights no fewer than the five most common complaints and the five most common suggestions, each with clear frequency metrics.
• All code or processing steps are included or reproducible so we can update the analysis later.

Once I receive the above and the findings make sense when spot-checked against the raw comments, the project is complete.