Sentiment Analysis
Understand customer emotions and opinions through AI-powered sentiment analysis.
How It Works
Text Analysis
AI analyzes feedback text, examining words, phrases, and language patterns
Emotional Context
System identifies emotional indicators and contextual phrases
Sentiment Scoring
Each feedback receives a sentiment score with intensity measurement
Trend Analysis
Aggregates scores to identify patterns and trends over time
Dashboard Features
Sentiment Overview
Visual breakdown of positive, negative, and neutral feedback
- Overall sentiment distribution
- Sentiment trends over time
- Category-wise sentiment
Trend Analysis
Track sentiment changes across different timeframes
- Daily/weekly/monthly trends
- Comparative analysis
- Anomaly detection
Filtering & Segmentation
Analyze sentiment across different dimensions
- Channel-wise breakdown
- Customer segment analysis
- Feature-specific sentiment
Best Practices
Look Beyond Scores
Read actual feedback samples to understand context and nuances
- Review a mix of positive and negative feedback
- Look for patterns in customer language
- Consider cultural and contextual factors
Track Trends
Monitor sentiment changes over time to measure improvement
- Set baseline measurements
- Compare periods after changes
- Identify seasonal patterns
Take Action
Use sentiment insights to drive improvements
- Share insights with relevant teams
- Create action plans for negative trends
- Celebrate positive improvements
Pro Tips
- • Combine sentiment analysis with other metrics for a complete picture
- • Set up alerts for sudden changes in sentiment
- • Use sentiment data to identify training opportunities
- • Share sentiment insights with relevant teams regularly