Know
See what your reviews are telling you.
Review analytics, competitor benchmarks, and AI-powered insights in one dashboard. Know where you stand, what's hurting your rating, and what to fix first.
How it works
Connect your profile
Same Google connection you use for replies. Analytics start populating immediately from your review history.
Add competitors
Enter up to 5 competitor names. We pull their public review data and show side-by-side comparisons updated weekly.
Read the insights
The AI scans your reviews for recurring themes and ranks them by impact. You see exactly which issues to address and which strengths to double down on.
Sentiment analysis of reviews
Sentiment analysis of reviews reads the text of every customer review and scores it as positive, negative, or neutral, overall and per topic. It surfaces what star ratings hide: a 4-star review can contain a pricing complaint, and a stable average can mask a service problem that started last month. ReviewTactic runs this analysis automatically across all your review sources.
How sentiment analysis of reviews works
The process has three steps: natural language processing splits each review into statements, classifies the emotional tone of each one, and tags the topic it refers to, such as service, pricing, wait time, or cleanliness. One review can carry several sentiments: praise for the staff and a complaint about parking are scored separately.
Scores are then aggregated over time and by topic. That aggregation is the point. A single review is an anecdote; three hundred reviews scored by topic show that food sentiment is stable while service sentiment has dropped for six weeks, which no star average will tell you.
ReviewTactic compresses this into a Reputation Index, a composite score out of 100, backed by a sentiment timeline and a topic heatmap, so you see both the headline number and the specific topic moving it.
What review sentiment data reveals
Trends before ratings move. Star averages are slow: hundreds of old 5-star reviews cushion new problems for months. Sentiment on recent reviews turns negative first, so a topic-level dip in weeks one and two becomes your early warning instead of a rating drop in month four.
Competitor gaps. Running the same analysis on competitors' public reviews shows where they are weak. If their delivery sentiment is poor and yours is strong, that is a claim worth putting in your marketing. ReviewTactic benchmarks your scores against competitors you pick.
What to fix first. Topic sentiment ranks problems by how often they appear and how negative they are. ReviewTactic's AI advisor turns that ranking into a plain recommendation, such as which topic is costing you the most goodwill right now, so analysis ends in an action rather than a chart.
FAQ
What data does the dashboard show?
Review volume over time, average rating trend, response rate, sentiment breakdown, and a competitor comparison panel. Growth and Managed plans add AI-generated insights that tell you what to fix first.
How does competitor benchmarking work?
Enter up to 5 competitors. We pull their public Google review data – count, rating, recent velocity – and show how you compare. Updated weekly.
What are AI insights?
The AI reads your recent reviews, spots recurring themes (wait times, staff, cleanliness), and ranks them by frequency and sentiment. You see which issues hurt your rating most and which strengths to highlight.
Can I export the data?
Yes. Every chart and table can be exported as CSV or PDF. Useful for client reports or internal meetings.
What is sentiment analysis of reviews?
Sentiment analysis of reviews is the automated reading of customer review text to classify each statement as positive, negative, or neutral and tie it to a topic like service, pricing, or cleanliness. It converts unstructured review text into measurable scores you can track over time. This reveals problems and strengths that star ratings average away.
How is review analytics different from just reading reviews?
Review analytics scores and aggregates every review, so patterns show up that reading one review at a time misses. A person reading reviews remembers the loudest ones; analytics weighs all of them and shows, for example, that wait-time complaints doubled this quarter. It also tracks trends across months and compares you against competitors, which manual reading cannot do at any real volume.
What should online reputation analytics include?
Online reputation analytics should include four things: a composite score you can track month over month, sentiment broken down by topic, trend lines that show direction rather than a snapshot, and competitor benchmarks for context. ReviewTactic covers these with its Reputation Index out of 100, topic heatmap, sentiment timeline, and competitor comparison, plus an AI advisor that states what to fix first.
See your review data in minutes
Connect Google, add competitors, and the dashboard fills itself.
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