Sentiment Analysis
Understand how your customers feel. AI-powered sentiment analysis detects emotional tone in every message, helping you respond appropriately.
What Is Sentiment Analysis?
Sentiment analysis uses AI to determine the emotional tone of customer messages:
- Positive: Happy, satisfied, grateful
- Neutral: Informational, matter-of-fact
- Negative: Frustrated, angry, disappointed
Visual Indicators
| Sentiment | Icon | Color |
|---|---|---|
| Positive | 😊 | Green |
| Neutral | 😐 | Gray |
| Negative | 😟 | Red |
Where to See Sentiment
AI Sidebar
Open any ticket to see overall sentiment in the AI Insights panel.
Per-Message Indicators
Individual messages can show sentiment badges indicating mood for that specific message.
Ticket List
Tickets with negative sentiment may show warning indicators.
Dashboard
Analytics show sentiment trends across your support.
Confidence Scores
Sentiment includes a confidence percentage:
| Confidence | Meaning |
|---|---|
| 90%+ | Very clear sentiment |
| 70-89% | Reasonably confident |
| 50-69% | Mixed signals |
| Below 50% | Uncertain |
Example: "Negative (85%)" means the AI is quite confident the customer is unhappy.
How Sentiment Is Detected
What AI Analyzes
- Word choice ("frustrated" vs "curious")
- Punctuation patterns (!!!!)
- Capitalization (ALL CAPS)
- Phrases ("I've been waiting forever")
- Context from conversation history
Per-Message vs Overall
- Per-message: Each message analyzed individually
- Overall: Latest sentiment + conversation trajectory
Sentiment Changes
Sentiment updates throughout the conversation:
Message 1: "Where's my order?" → Neutral
Message 2: "It's been 2 weeks!" → Negative
Message 3: "Thanks for the update" → Positive
Using Sentiment Effectively
Calibrate Your Tone
Match your response to customer sentiment:
| Customer Sentiment | Your Response |
|---|---|
| Positive | Warm, conversational |
| Neutral | Professional, efficient |
| Negative | Empathetic, apologetic, solution-focused |
Prioritize Negative Sentiment
Negative sentiment often indicates:
- Urgent issues
- Risk of churn
- Need for escalation
- Potential public complaint
Monitor Sentiment Shifts
Watch for:
- Positive → Negative: Something went wrong
- Negative → Positive: Your resolution worked
- Stable negative: May need escalation
Sentiment-Based Workflows
Automated Routing
Create automations based on sentiment:
Example automation:
- Trigger: Ticket sentiment is Negative AND confidence > 80%
- Action: Add tag "needs-attention" and notify manager
SLA Adjustments
Consider faster response for negative sentiment tickets.
Escalation Triggers
Auto-escalate when sentiment is highly negative + VIP customer.
Responding to Different Sentiments
Positive Sentiment
Customer feels good. Maintain the relationship:
- Thank them for positive feedback
- Maintain friendly tone
- Ask for reviews if appropriate
- Resolve efficiently
Neutral Sentiment
Customer is task-focused. Be helpful:
- Answer questions directly
- Provide clear information
- Don't over-apologize
- Keep it professional
Negative Sentiment
Customer is frustrated. Turn it around:
- Acknowledge their frustration
- Apologize sincerely
- Take ownership
- Provide clear resolution path
- Follow up to confirm satisfaction
Sentiment Analytics
Team Dashboard
View sentiment metrics in Analytics:
- Overall sentiment distribution
- Sentiment trends over time
- Sentiment by agent
- Sentiment by ticket type
Useful Insights
- Rising negative sentiment: Process or product issue?
- Agent sentiment scores: Training opportunities?
- Sentiment by category: Which issues cause frustration?
Accuracy & Limitations
Generally Accurate For
- Clear emotional language
- Standard support scenarios
- English language
- Written communication
May Misread
- Sarcasm ("Great, another delay")
- Technical discussions (neutral misread as negative)
- Cultural differences
- Very brief messages
When to Override
If sentiment seems wrong:
- Trust your judgment
- Consider the full context
- AI provides guidance, not certainty
Tips for Using Sentiment
- Check sentiment first: Before responding, know the mood
- Don't over-rely: It's a signal, not gospel
- Track trends: Individual tickets + overall patterns
- Respond appropriately: Match your tone to theirs
- Celebrate improvements: Note when you turn negative to positive
Troubleshooting
"Sentiment says positive but customer seems upset"
- AI may have caught different signals
- Sarcasm can be misread
- Trust your reading of the situation
"Sentiment not showing"
- Very short messages may not analyze
- Check AI settings
- Try refreshing the ticket
"Sentiment changes unexpectedly"
- Based on latest message
- May aggregate entire conversation
- Check which message triggered change
Privacy Note
Sentiment analysis:
- Processes ticket content only
- Does not store emotional profiles
- Used only for this ticket's context
- Complies with privacy regulations