Analyze user feedback sentiment without training examples, using only sentiment definitions. ## Feedback to Analyze {{feedback_text}} ## Sentiment Categories - **Positive**: User expresses satisfaction, appreciation, or success - **Negative**: User expresses frustration, disappointment, or failure - **Neutral**: User provides factual information without emotional valence - **Mixed**: User expresses both positive and negative sentiments ## Analysis Instructions 1. **Read the feedback** carefully, noting emotional indicators. 2. **Identify sentiment signals**: - Explicit emotion words - Implicit tone - Context clues - Emphasis patterns 3. **Assess intensity** (low/medium/high). 4. **Extract key themes** driving the sentiment. **Output Format:** ```json { "sentiment": "positive|negative|neutral|mixed", "intensity": "low|medium|high", "confidence": 0.0-1.0, "key_themes": ["theme1", "theme2"], "reasoning": "Brief explanation" } ```
Zero-Shot Sentiment Analysis for Feedback
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Zero-shot sentiment analysis for user feedback with category definitions, intensity assessment, and theme extraction.
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Category
AnalysisUse Cases
Sentiment analysisFeedback processingUser research
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claude-sonnet-4-20250514gpt-4o
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