Consumer & Brand
Reviews & AI SentimentAI
Score millions of reviews by location and dish — and turn raw star ratings into aspect-level insight you can act on.
// sentiment scored at aspect & location level
| outlet | aspect | sentiment | score | n |
|---|---|---|---|---|
| MG Road | Food | Positive | 0.81 | 1,204 |
| MG Road | Delivery | Negative | 0.38 | 640 |
| HSR Layout | Value | Neutral | 0.55 | 902 |
Overview
What is AI review sentiment analysis?
AI review sentiment analysis is the use of natural-language models to read customer reviews and classify how people feel — overall and about specific aspects like food, service, value and delivery. For food brands, it converts millions of unstructured reviews into structured, location-level insight.
A four-star average hides more than it reveals. Two outlets with identical ratings can have completely different problems — one praised for food but slow on delivery, another loved for value but weak on service. Aspect-level sentiment separates those signals so brand and operations teams know exactly what to fix and where. Tracking sentiment over time also shows whether a menu change, price move or incident actually shifted perception. Processed at scale across multiple languages and locations, this turns the review pile every restaurant ignores into a continuous, comparable measure of customer experience.
Capabilities
Beyond the star rating
AI that reads what customers actually said, at scale.
Sentiment scoring
Every review scored positive, neutral or negative with confidence.
Aspect extraction
Split feedback into food, service, value, delivery and ambience.
Location rollups
Aggregate sentiment by outlet, city or zone.
Trend tracking
Watch sentiment move after a menu change or incident.
Multi-language
Reviews normalized and scored across languages.
Theme detection
Surface recurring complaints and praise automatically.
What's included
Every sentiment engine ships with
Standard fields and outputs. Anything here can be extended, trimmed or customized to your scope.
Methodology
How does AI review sentiment analysis work?
From raw reviews to aspect-level insight, in four steps.
1 · Collect reviews
Reviews are gathered across the platforms and locations you track.
2 · Score sentiment
Each review is classified overall and by aspect.
3 · Roll up & theme
Scores aggregate by outlet and surface recurring themes.
4 · Deliver
Structured sentiment arrives as CSV, JSON or API.
Who it's for
Get answers like
Real questions our sentiment engine answers for teams across the food economy.
"What do customers love or hate?"
Aspect-level sentiment by outlet.
"Did the new menu land?"
Sentiment trend before vs after launch.
"Which location is slipping?"
Location-ranked sentiment scores.
Why FoodDataScrape
Why teams choose us for this
- Aspect-level scoring, not just an overall star average
- Location rollups across outlets, cities and zones
- Multi-language handling at scale
- Free proof-of-concept on your brand's reviews
Formats
CSV, JSON or direct API — pick what plugs into your stack. Custom schemas on request.
Refresh cadence
One-time pull, daily, weekly or real-time feeds, scoped to how fast your decisions move.
Integration
Drop into BI tools, data warehouses or apps. Webhooks and scheduled exports supported.
Questions
Frequently asked questions
Millions — sentiment and aspect scoring run at scale across platforms and languages.
Yes — multi-language reviews are normalized and scored.
Yes — sentiment trends can be tracked around launches, incidents or campaigns.
Food, service, value, delivery, ambience and any custom aspects you define.
Major review and delivery platforms across our 15 markets.
Per review and aggregated by outlet, with aspect, sentiment and theme fields.
Hear what your reviews are really saying
Share the brand or outlets you care about. We'll return a free sample aspect-sentiment report.
