Theme and topic clustering
Similar reviews are grouped automatically, which surfaces the most discussed problems and expectations.
Reviews are labelled by theme, sentiment, urgency, product area and action type. Teams see what needs to change without reading thousands of free-text records.
“The product is good but the delivery arrived very late.”
“The support team got back to me quickly.”
“The app screen could be simpler.”
Brands analyse review streams continuously and see customer expectations, product problems and campaign impact early.
Similar reviews are grouped automatically, which surfaces the most discussed problems and expectations.
Negative sentiment, crisis risk and reviews that need a fast reply are prioritised.
Action labels are produced for product, support, marketing or operations teams.
Reviews, messages or open-ended answers are sent to the service over the API.
Sentiment, topic, urgency and action area are determined.
Trends flow into dashboards, reports or product management tools.
NLP
theme extraction
Live
sentiment tracking
Webhook
action output
The review analysis service automatically tags texts such as product reviews, survey answers and social media messages by theme, sentiment (positive, negative, neutral) and urgency. Results return to your systems through a REST API.
The service uses models trained for the agglutinative structure of Turkish, irony and spelling mistakes; it evaluates sentence context rather than matching a word list.
E-commerce product reviews, marketplace ratings, app store reviews, open-ended survey answers and support messages can all be sent through the API.
We review the channels, data and integrations you already have, then set out the right MVP flow.