Review Sentiment Analysis: Catch Drops Before Returns

A weekly sentiment report for a four product catalog. Two quality alerts already flagged. One product dropped from 0.74 to 0.31 in a single week. The other sitting below the minimum threshold. Both caught before Monday morning. Customer experience lead at a midsize DTC retailer. 842 reviews came in last week across four product lines. The sentiment spreadsheet has not been updated since February. Each product needs its reviews read, scored, and compared against last week. That is 12 to 16 hours of reading. She spot checkcks the worst rated product and skims the rest. A three-star review mentions Bluetooth drops and a charging case that stopped working after 2 days. It reads like a minor complaint. It is actually a defect pattern hiding inside qualified praise. Four other reviews describe the same failure differently. The review dashboard shows 3.8 stars, same as last week, but the composition underneath changed completely. A Slack channel catches the one-star reviews. It misses the three-star reviews where customers write that something is decent, then describe a hardware failure in the next sentence. Every review gets read and scored against last week's baseline. The AI agent classifies sentiment from the actual language, not just the star count. Whether you are monitoring a DTC product catalog, subscription box, SKUs, or marketplace listings, the structure is the same. Weekly scores compared, trends surfaced. The quality alert section ranks by severity. Earbuds dropped 0.43 in 1 week, flagged high. Smart Lamp sits below the minimum score, flagged medium. The performance table shows all four products with current and previous scores side by side. And the recommendation section gives three actions tied to this week's specific themes. Bluetooth drops, charging case failure, connectivity issues. On the left, a star average that stayed flat while a product crisis built underneath. On the right, a scored report that caught the 0.4 43 drop. Extracted the complaint themes and recommended next steps. Trends caught weekly, not monthly. Lassa.ai. See what review sentiment analysis looks like for your catalog. Your reviews read. Your Monday reclaimed.