Automated Text Annotation
AI text tagging: sentiment, intent, and more
Automated Text Annotation
AI text tagging: sentiment, intent, and more
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Description
🏷️ **One-line value**: Upload text data, and AI automatically handles multi-dimensional annotation including classification, sentiment, and intent, greatly reducing manual labeling time. 🔍 **Core features**: - **Multiple annotation tasks**: Supports single-label classification, multi-label classification, sentiment analysis, intent recognition, entity extraction, custom scoring. - **Smart label system**: When no labels are provided, AI automatically analyzes the data and recommends a classification scheme. - **Three annotation modes**: Zero-shot (no training), few-shot (10-100 examples), iterative refinement (continuously improving quality). - **Quality control system**: Confidence scoring, consistency checks, contradiction detection, edge-case flagging. - **Dual-format output**: Annotation results in Excel + quality report in Markdown. 🎯 **Use cases**: - 📊 User comment analysis: Automatically label sentiment (positive/negative/neutral). - 🛒 Customer service ticket classification: Automatically identify intents such as inquiry, complaint, return, suggestion. - 🔍 Social media monitoring: Automatically tag topics (tech/entertainment/sports/finance). - 📋 Data governance: Convert unstructured text into structured labeled data. - 🧪 Training dataset construction: Quickly generate large-scale labeled data. 👩💼 **Who it's for**: Data annotators, product managers, market analysts, AI engineers, researchers, content operators—anyone who needs to process text classification in bulk.
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Information
- Version
- v2
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
