AI Analytics
AI-Powered Response Analysis
Stop reading spreadsheets. Let AI analyze sentiment, discover themes, and surface actionable insights from your survey responses automatically.
Quick Reference
| Feature | What it does | Output |
|---|---|---|
| 😊 Sentiment Analysis | Detects emotions in open-text responses | Positive / Neutral / Negative + 8 emotions |
| 🏷️ Theme Detection | Groups similar responses into topics | Auto-generated topic clusters |
| 💡 AI Insights | Finds patterns and anomalies in data | Warnings, Trends, Opportunities |
| 🔍 Semantic Search | Search by meaning, not just keywords | Ranked response matches |
Sentiment Analysis
Sentiment Analysis
Emotion DetectionAI analyzes the emotional tone of open-text responses, classifying them into sentiment categories and detecting specific emotions.
Interactive Sentiment Distribution
Emotions Detected
Our AI can detect 8 distinct emotions in responses:
Aspect-Based Analysis
Beyond overall sentiment, AI can detect sentiment toward specific aspects mentioned in responses:
"The product quality is amazing, but shipping took forever and customer support was unhelpful."
Theme Detection
Theme Detection
Auto-ClusteringAI automatically groups similar responses together to reveal common themes and topics, powered by vector embeddings and clustering algorithms.
How It Works
Interactive Theme Distribution
Themes are displayed with response counts and can be clicked to filter responses. The visualization above shows a typical distribution of themes detected from customer feedback.
AI Insights
AI Insights
Pattern RecognitionAI automatically generates actionable insights by analyzing patterns in your response data.
Insight Types
"Negative sentiment has increased 15% compared to last month"
"Customers mentioning 'delivery speed' are 3x more likely to recommend"
"23% of respondents requested mobile app — consider prioritizing"
"Customer satisfaction score reached all-time high of 4.8/5"
Semantic Search
Semantic Search
Vector-PoweredSearch through responses by meaning, not just exact keyword matches. Powered by pgvector and text embeddings.
How It Works
Search Examples
| Search Query | Finds Responses Like |
|---|---|
| "unhappy with service" | "Customer support was terrible", "Very disappointed", "Will not buy again" |
| "pricing concerns" | "Too expensive", "Not worth the cost", "Overpriced compared to competitors" |
| "product suggestions" | "Would love to see mobile app", "Please add dark mode", "Need better integrations" |
| "positive feedback" | "Absolutely love it!", "Best purchase ever", "Highly recommended" |
Semantic search works best with natural language queries. Instead of searching for "bad", try "negative customer experiences" for more comprehensive results.
Best Practices for Analytics
Analytics work best with 50+ responses. Theme detection improves significantly with more data points.
Include at least one open-text question. AI analytics shine when analyzing free-form responses.
Re-run analytics periodically as new responses come in to catch emerging trends and shifts.
Export key findings to your team. AI insights are valuable only when they drive decisions.