Blog
Guides and insights on AI voice agents, call centers, and calling at scale.
Glossary & Guides
What Is Sentiment Analysis on Calls?
Published
Every customer call carries emotion. A customer might begin frustrated and end relieved, or start calm and become increasingly angry. Understanding these emotional shifts can help businesses improve service, support staff, and spot problems early. Sentiment analysis is technology designed to detect them.
The Definition
Sentiment analysis is the use of technology, usually AI, to estimate the emotional tone of a conversation, typically classifying it as positive, negative, or neutral. In call centers, it's applied to phone calls, either as they happen or afterward.
How Sentiment Analysis Works on Calls
Sentiment analysis on calls generally uses two kinds of information.
What's said
The call is first converted into text using speech recognition. The text is then analyzed for words, phrases, and patterns associated with emotion.
For example:
- "That's really helpful, thank you so much" suggests positive sentiment.
- "This is the third time I've called about this" suggests frustration.
- "I'd like to update my address" is usually neutral.
Modern AI models analyze not just individual words but the overall meaning of sentences and how they fit into the conversation.
How it's said
Some systems also analyze acoustic features of the voice, such as:
- Pitch and changes in pitch.
- Volume, including raised voices.
- Speaking rate, such as speaking faster when agitated.
- Pauses and silence.
- Overtalk, when people speak over each other.
Combining what's said with how it's said can give a fuller picture than either alone.
Tracking sentiment over time
Rather than giving a single score for a whole call, many systems track sentiment throughout the conversation. This shows how a customer's mood changed, for example, starting negative and ending positive after their issue was resolved, which can be more informative than an overall average.
What Businesses Use It For
Spotting calls that need attention
Real-time sentiment analysis can alert supervisors when a call is becoming heated, allowing them to listen in or offer support.
Prioritizing quality reviews
Instead of reviewing random calls, quality teams can focus on calls with strongly negative sentiment, which are more likely to reveal problems.
Understanding customer experience at scale
Analyzing sentiment across thousands of calls helps businesses see which topics, products, or processes are linked to frustration.
Finding root causes
If calls about a particular issue consistently show negative sentiment, it may point to a confusing policy, a product fault, or a process that needs fixing.
Coaching
Calls where sentiment improved significantly can be used as examples of good practice. Calls where it declined can highlight coaching opportunities.
Measuring changes
Businesses can track whether sentiment improves after making changes, such as introducing a new process or training.
The Limitations
Sentiment analysis is useful, but it's an estimate, not a precise measure of feelings.
Sarcasm and irony. "Oh, great, another problem" may be classified as positive when it's clearly negative.
Context. Words can mean different things in different situations. A customer saying "I'm so sick of this" is negative, but in some contexts "sick" can be positive slang.
Cultural and individual differences. People express emotions differently. Some speak loudly and energetically when perfectly happy; others stay calm when very upset.
Accents and speech recognition errors. If speech is transcribed incorrectly, sentiment analysis may be wrong. Accuracy can vary for different accents and speaking styles.
Topic vs emotion. A customer calling about a bereavement may sound sad, but that doesn't mean the call went badly.
Agent vs customer. Systems need to distinguish who is speaking, since an agent's calm tone and a customer's frustration mean different things.
Using Sentiment Analysis Responsibly
Treat it as a guide, not a verdict
Sentiment scores are best used to point people toward calls worth reviewing, not to make final judgments about calls or staff.
Be cautious about judging staff
Using sentiment scores to evaluate agents can be unfair, since customers may be upset before the call starts or about issues the agent can't control. Any judgments should involve human review and context.
Check accuracy and fairness
Businesses should test whether sentiment tools work accurately across different customers and staff, including different accents and speaking styles.
Respect privacy and legal limits
Analyzing emotions involves personal data, and rules in some places limit how it can be used. In the European Union, for example, the AI Act prohibits AI systems that infer the emotions of people in the workplace, with limited exceptions such as medical or safety reasons. This affects how such tools can be used on staff. Businesses should check the rules that apply where they operate.
Be transparent
Staff should know if sentiment analysis is used on their calls and how the results are used.
Listening at Scale
Sentiment analysis gives businesses a way to listen to the emotional side of thousands of conversations at once. Used thoughtfully, it helps spot calls that need support, uncover what frustrates customers, and measure the effect of improvements. But it's an estimate of human emotion, not a perfect reading of it. The most valuable use of sentiment analysis is as a starting point for human understanding, pointing people toward the conversations that most deserve their attention.