Amazon Planter Review Analysis

I need a clear, data-driven picture of what buyers have been saying about my laser-cut metal garden planters on Amazon during the last six months. The sole focus is the review section—sales trends and demographic breakdowns are outside the scope.

Your task is to pull every English-language star rating and written review from that six-month window, distill the recurring suggestions customers offer for improving the product, and translate those findings into concrete, prioritized action items I can hand straight to my manufacturing and listing teams. Sentiment classification, keyword clustering, and any NLP techniques that surface hidden patterns are all welcome, provided the final insights are practical and easy to act on.

Deliverables
• A concise report (PDF or Google Doc) summarizing the most common improvement suggestions, each backed by representative review excerpts and frequency counts.
• A spreadsheet of the raw review data you collected, tagged by theme and sentiment.
• A one-page executive summary highlighting the top three changes most likely to lift ratings.

Please base every recommendation strictly on reviews posted within the past six months, and keep the language of the report straightforward enough for non-technical stakeholders.