What Is Sentiment Analysis and How to Use It on Customer Reviews
Basics3 minInnovialabs editorial team
In short
Sentiment analysis automatically labels feedback as positive, negative or neutral, and aspect-based analysis shows exactly what customers praise or criticize. The value appears when results are grouped by topic and turned into concrete tasks for your team.
The short answer
Sentiment analysis is the automatic detection of the emotional tone of a text: positive, negative or neutral. It is applied to reviews, support tickets, comments and surveys so you do not have to read thousands of messages by hand.
A single label like "this review is negative" does not tell you much, though. Far more useful is aspect-based sentiment analysis: it splits feedback into topics and scores each one separately.
Overall sentiment vs aspect-based analysis
Example review: "Delivery was fast, but the courier was rude and the app keeps crashing."
| Approach | Result |
|---|---|
| Overall sentiment | Negative |
| Aspect-based | Delivery: positive, courier: negative, app: negative |
The first tells you the customer is unhappy. The second tells you what to fix and what already works.
How LLMs handle the hard cases
Classic models relied on word lists and patterns and made many mistakes. Large language models understand context much better:
- Sarcasm. "Great, third time waiting a week for my order" is usually read as negative, even though it contains "great". Not always, though: short ironic phrases with no context remain hard.
- Mixed languages. Reviews in Uzbekistan often mix Russian, Uzbek in Latin and Cyrillic script, and English terms. Modern LLMs handle such texts without a separate model for each language.
- Slang and typos. Context helps recover the meaning despite errors.
- Hidden negativity. "Well, it's okay overall" can be flagged as lukewarm rather than positive.
There are limits: results depend on how the prompt is phrased, and cost matters at large volumes. Regularly check a sample of the model's answers by hand.
How to implement it, step by step
- Collect sources. Marketplace reviews, Google and Yandex Maps, Telegram messages, CRM tickets.
- Define a list of aspects for your business, for example price, quality, delivery, support, app.
- Write a prompt with a fixed output format so results can be stored in a table.
- Test on a sample. Label a few dozen reviews by hand and compare them with the model's output.
- Automate. New reviews get analyzed as they arrive and land in a dashboard or your CRM.
An example of structured output that is easy to store in a database:
{
"overall": "negative",
"aspects": [
{"aspect": "delivery", "sentiment": "positive"},
{"aspect": "courier", "sentiment": "negative"},
{"aspect": "app", "sentiment": "negative", "issue": "crashes"}
]
}
Turning results into decisions
- Watch trends, not snapshots. What matters is whether negativity about delivery grows after you switch couriers.
- Rank problems by frequency and impact: what comes up most and what is most tied to negative reviews.
- Assign owners. App complaints go to development, courier complaints go to logistics.
- Respond to critical reviews fast. Set up an alert for strongly negative feedback.
- Check the effect. After a fix, see whether sentiment for that aspect changes.
Common mistakes
- Stopping at one overall score with no aspects.
- Not testing model quality on your own data.
- Building a report nobody reads and that never leads to tasks.