Review Sentiment
A star rating tells you something is wrong. It won’t tell you what. Read review text at scale to surface recurring themes, complaints, and feature requests, and turn scattered feedback into a short list worth acting on.
Example Questions
- “What features do customers wish this product had?”
- “What are the most common topics in reviews?”
- “What do reviews say we do better than competitors?”
- “Compare review sentiment for our product against the top 3 competitors — what do they praise that we get criticized for?”
- “Has a specific complaint theme increased in frequency since our last listing update, suggesting the change backfired?”
- “Which negative review themes correlate most strongly with declining conversion rate, versus ones that are just noise?”
Frequently Asked Questions
It reads Amazon review text at scale and groups it into recurring themes: complaints, praised features, and feature requests. It turns thousands of scattered reviews into a short, ranked list of what customers care about.
It reads across available reviews at scale rather than a small sample, so themes reflect the overall pattern rather than a few outliers.
Yes. It pulls review themes for competitor products too, so you can see what customers praise them for that they criticize you for, and the reverse.
Yes. It compares sentiment before and after an update, so you can see whether a listing or product change improved perception or created a new complaint.
In Ask Cobalt inside Jungle Scout Cobalt, and through the Jungle Scout MCP in AI tools like Claude and ChatGPT.
