"Terrible service, I've been waiting for 3 days!" vs. "Thank you very much, problem solved quickly." You can see the difference at a glance. But when you have thousands of messages a day, how can you automatically detect dissatisfied customers?
That's wheresentiment analysis comes in. In this guide, find out what it is, how it works, and how to use it in your customer service department.
Sentiment analysis is an artificial intelligence technique that automatically identifies the emotional tone of a text: positive, negative, or neutral.
It is an application of NLP (Natural Language Processing) that goes beyond simply understanding content to capture the emotion behind it.
Sentiment analysis answers the question: How does this person feel?
The simplest form: positive or negative. Useful for a quick initial sorting.
Positive, negative, or neutral. Neutral allows you to distinguish factual messages without any particular emotion.
A continuous score (e.g., -1 to +1) that measures the intensity of the feeling. "Disappointed" ≠ "Furious."
Beyond positive/negative: joy, sadness, anger, surprise, fear... More subtle but more complex to implement.
Identify sentiment on different aspects of the same message: "The product is great (positive) but the delivery was terrible (negative)."
A dictionary associates words with sentiment scores. "Excellent" = +2, "Terrible" = -2. The overall score is the sum of the word scores.
Advantages: simple, explainable, no training data required.
Limitations: does not handle context, irony, or negations ("not bad" = positive).
A model learns from annotated examples (texts + corresponding sentiments).
Advantages: adapts to your context, better management of nuances.
Limitations: requires labeled data for training.
Modern language models understand context, irony, and innuendo.
Advantages: high performance, handles complex cases.
Limitations: more resource-intensive.
A ticket with a very negative sentiment must be handled as a priority. Automatic analysis makes it possible to detect emotional emergencies.
Immediate notification when a VIP customer or public message expresses strong negative sentiment.
Highly negative messages can be routed to experienced agents or managers.
Track changes in average sentiment over time: are customers becoming more satisfied or dissatisfied?
Assess sentiment at the beginning vs. the end of the conversation: did the agent succeed in improving the customer's mood?
A customer with consistently negative feelings is a candidate for leaving. Analysis allows you to intervene before they leave.
Klark integrates sentiment analysis to automatically detect dissatisfied customers and prioritize the most sensitive requests.
"Well done, 10 days to deliver a package, that's really great!" The words are positive, but the sentiment is negative. Difficult to detect automatically.
"That's crazy" can be positive (admiring) or negative (dismayed) depending on the context.
Abbreviations, emojis, spelling mistakes... The language customers actually use is far removed from academic language.
Every language has its own expressions and emotional register. A French model does not work as such in English.
The same message can contain both positive and negative elements. An overall analysis may mask this nuance.
Real-time prioritization? Comprehensive trend analysis? The use case guides the choice of tool.
Take a sample of tickets, analyze them manually, compare with the model's predictions.
At what score do you trigger an alert? Specific routing? Escalation?
Sentiment analysis is most valuable when integrated into your tools (CRM, help desk).
Check the accuracy regularly and adjust if necessary.
Sentiment analysis can complement or enrich your traditional indicators:
No model is perfect. Regularly check classifications on samples.
"Urgent" is not negative, but may require priority action. Adapt to your context.
Detecting dissatisfaction without a response process is useless.
These are complementary metrics, not substitutes. Sentiment is automatic but less reliable than explicit feedback.
Sentiment analysis processes personal data. Ensure you are GDPR compliant.
Zendesk, Freshdesk, and others now include native sentiment analysis.
Tools such as Klark analyze sentiment in the specific context of customer service, with greater relevance.
Good models achieve 80-90% accuracy on plain text. They are less reliable when it comes to irony, sarcasm, or ambiguous messages.
Yes, through transcription (speech-to-text) and then text analysis. Some tools also analyze the tone of voice.
No, it complements them. Sentiment analysis is automatic and comprehensive, while surveys provide more structured but partial feedback.
Multilingual models exist and work well, but a model dedicated to your primary language will be more accurate.
Sentiment analysis is a powerful tool for understanding the emotional state of your customers on a large scale. It allows you to prioritize, alert, and act before dissatisfaction turns into attrition.
The keys to effective sentiment analysis:
Want to automatically detect your dissatisfied customers? Discover how Klark can help you.
