Automation
Review Sentiment Analysis: Understand What Customers Are Really Saying
A 3-star review average tells you customers are not thrilled. It does not tell you whether the problem is the product, the service, the pricing, or the delivery experience. Deploy this automation and every review is classified by topic and sentiment the moment it arrives, so you can see exactly what is driving the score.
How it works
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New review parsed for sentiment the instant it posts
Every new review parsed for sentiment and theme as it lands, across every platform you connect.
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Sentiment tagged automatically
Each review classified by topic and tone within seconds of posting.
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Negative themes surfaced first
Every review sorted by sentiment the instant it lands:
✓ Positive sentiment logged to the topic trend.
→ Negative sentiment spikes flagged for the owning team.
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Sentiment trend dashboard
Topic-level sentiment over time, updated as reviews arrive.
Sample survey questions
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Reviews classified this period
Auto-tagged by topic and sentiment as they arrive
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Top negative-sentiment topic
The specific theme driving your rating down right now
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Sentiment trend by topic
Tracked week over week, per category
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Alert threshold
Notifies your team when negative sentiment on a topic crosses your set level
Star ratings are a compression of customer experience into a single number, and like all compressions, they lose the information that matters most. A 3-star review about excellent product quality but terrible customer service tells a completely different story from a 3-star review about poor product quality and great customer service. The average of those two reviews is 3 stars either way, and the star average tells you nothing about what to fix. Deploy FeedbackRobot's review sentiment analysis automation and every incoming review is processed the moment it arrives. The content is classified by topic, whether the reviewer is commenting on staff, product quality, price, speed, or communication. The sentiment within each topic is scored. A review that is positive about the product but negative about delivery speed contributes to two separate trend lines, not one averaged score. Over weeks and months, the pattern data reveals which specific dimensions of the customer experience are driving your rating up or holding it down. A business that has been collecting 4-star reviews for six months but losing ground to a competitor can see in the sentiment data whether the gap is in a specific service area that the competitor has improved, and target their response accordingly.
Frequently asked questions
How is this different from the sentiment-analysis template?
This one is scoped specifically to reviews; the sentiment-analysis template also pulls in survey responses, so it covers a broader set of feedback sources.
What does classified by topic actually mean in practice?
Each review gets tagged against categories you define, staff, product, price, speed, communication, so a single review mentioning both great product and slow delivery contributes to two separate topic trend lines, not one blended score.
Can this tell me which specific topic is dragging my rating down?
Yes, that's the core value, since a raw star average can't distinguish a product problem from a service problem, but topic-level sentiment breakdown can.
Does this work across every platform I connect, or just one?
It processes reviews from every platform you connect, applying the same classification consistently rather than needing separate setup per platform.
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