Sentiment analysis reads customer emotions from conversations and categorizes them into positive, neutral, negative or upset level. Thanks to that, the customer service team prioritizes handling customers urgently and measures satisfaction levels automatically in real time. Zenify recorded 98.4% accuracy in AI processing.
TL;DR
- Sentiment analysis reads customer emotions from content.
- Classification: positive, neutral, negative.
- Angry customer → prioritize handling immediately.
- Measure emotional trends by week/month.
- Early warning of guests preparing to leave.
What is Sentiment analysis?
Sentiment analysis is an AI technique that identifies customer attitudes:
Level | Content example
Positive | "Shop's service is so good!"
Neutral | "Let me ask the price..."
Mild negative | "Delivery took too long, impatient"
Strong negative | "Boring, cheating, what kind of answer is that!"
Why do we need sentiment analysis?
1. Prioritize angry customers
Angry customers must be handled first and by competent people (see AI Routing).
Don't let angry customers wait for hours.
2. Detect customers early who want to leave
Emotions gradually decrease = risk of leaving.
AI warns for timely care (see AI re-care).
3. Automatic satisfaction measurement
No need for surveys, AI reads from real conversations.
Emotion trends by week, by channel, by employee.
4. Detect problems early
Many customers are negative about the same topic = system problem.
Root treatment before noise (see Negative feedback).
How does Sentiment analysis work?
Step | Description
1. Get conversation | Chat, comment, call (via text)
2. AI reads context | Understand tone, words, situations
3. Emotion classification | Assign positive/negative levels
4. Inject stream | Prioritize, alert, measure
5. Report | Trends, hot topics, which employees often encounter
How to use sentiment analysis effectively
Configure alarm threshold: which negative level needs immediate priority.
Linked to the processing flow: angry customer → transferred to good employee.
See trends, not just individual items: track the week/month.
Combining context: Negative emotions due to the wrong customer also need to be handled skillfully.
Compare by employee: find employees who need skill support (see Auto QA).