Sentiment Analysis
beginnerdataMin 16K context
Classifies the sentiment and emotional tone of text — reviews, support tickets, social posts, and survey responses. Supports document-level and aspect-based sentiment, returns confidence scores and representative quotes, and aggregates trends across large batches with themes and actionable insights.
Use Cases
- Classifying customer reviews as positive, negative, or neutral
- Aspect-based sentiment on product features
- Triaging support tickets by frustration level
- Tracking sentiment trends across a batch of survey responses
- Surfacing representative quotes for each sentiment theme
Example Prompt
Analyze the sentiment of the following customer reviews. For each review return: - overall sentiment (positive/negative/neutral) with a confidence score - aspect-based sentiment (e.g., price, quality, support) Then provide an aggregate summary: sentiment distribution, top themes, and 3 representative quotes. Reviews: ``` [paste reviews here] ```
Recommended Models
Compatible Tools
claude-codekiroany
Modalities
Input: text
→Output: text
Related Skills
Author
OpenModels Community