Emotion vs. Sentiment Measures: Understanding the Difference and Value
Customer experience (CX) and employee experience (EX) are more critical than ever for businesses aiming to build loyalty and drive growth. The old adage is still true – “look after your employees and they will look after your customers”…..
As organizations strive to understand and improve these experiences, they often turn to analytics tools to measure customer and employee feedback. Two key concepts frequently used in these efforts are emotion and sentiment measures. While they may seem similar, they capture different aspects of human communication. Understanding their distinctions and combined value can help businesses create more effective strategies for engagement and support. Sentiment is the ‘what’ is being said, emotion is the ‘how’.
What is Sentiment Analysis?
Sentiment analysis is the process of determining the overall positive, negative, or neutral tone of a text or conversation. It involves analyzing language to identify how someone feels about a topic or interaction. For example, in a customer service call, statements like “This is the best service I’ve ever had” would be classified as positive sentiment, while “I’m very disappointed with your product” would indicate negative sentiment.
How Sentiment is Measured:
- Keyword and phrase detection: Identifying words and phrases with inherent positive or negative connotations (e.g., “excellent,” “terrible”).
- Context-based analysis: Using machine learning models to understand how words function within sentences to determine sentiment.
Sentiment analysis is widely used to track metrics like customer satisfaction (CSAT), net promoter score (NPS), and expressions of dissatisfaction (EoD). It provides a high-level view of how customers perceive their experiences with a brand.
What is Emotion Analysis?
Emotion analysis goes deeper by using a Biometric engine on the call recording, detecting specific emotional states expressed in conversations. These emotions may include joy, anger, stress, sadness, engagement, and confidence. While sentiment gives a general tone, emotion analysis identifies the specific feelings behind that sentiment.
AutoInsights employs an advanced biometric emotion model that scores emotions by analyzing both verbal and non-verbal cues across interactions. This approach measures key emotional responses such as stress, engagement, confidence, and uneasiness, offering a nuanced view of emotional trends.
For example, a customer might say, “A $100 per month? That sounds fine” in a collections or sales call which would likely register as positive sentiment BUT the biometric emotions are low confidence, stress, and sadness. The distinction is critical for key use cases like Vulnerability, Sales, or Claims handling.
How Emotion is Measured in Market:
- Speech patterns: Variations in tone, pitch, and speed that indicate emotional shifts.
- Text analysis: Recognizing emotionally charged language (e.g., “delighted,” “furious”).
- Biometric analytics: AutoInsights’ model analyzes emotional “heartbeats” by capturing data every few seconds to monitor real-time changes in emotion.
Key Differences Between Emotion and Sentiment Measures
| Aspect | Sentiment | Emotion |
|---|---|---|
| Focus | Overall tone (positive, negative, neutral) | Specific feelings (e.g., stress, engagement, joy, sadness) |
| Measurement | Based on language context | Should be based on biometric data |
| Use Case | High-level feedback and trends | Deeper understanding of emotional reactions |
| Examples | “The service was disappointing.” | Emotion Score: Client “uneasy” Agent “Stress High” |
Why Both Measures Matter for CX and EX
Understanding both sentiment and emotion can provide a more holistic view of customer and employee experiences. Here’s how each contributes to organizational insights:
1. Identifying Emotional Hotspots
Emotion analysis can reveal moments of high emotional intensity in customer interactions. These emotional “hotspots” often represent critical moments that shape overall perceptions of a brand.
2. Enhancing Sentiment Trends with Context
While sentiment trends help track changes over time, they may lack the nuance needed for deeper insights. Emotion analysis can provide context for these trends, explaining why a particular sentiment is increasing or decreasing.
3. Improving Personalization and Empathy
By understanding both sentiment and emotion, businesses can tailor their responses to customers and employees more effectively.
4. Supporting Employee Well-Being
Emotion analytics isn’t just for customers. It can also be applied to employee interactions to assess engagement and well-being.
The Future of Experience Analytics
As AI technologies continue to advance, the integration of sentiment and emotion measures will become even more powerful.
Final Thoughts
While sentiment and emotion measures serve different purposes, their combined use can transform how businesses understand and improve experiences.
Join the Conversation: How do you currently measure customer or employee feedback? Share your thoughts in the comments below or connect with us on LinkedIn!
