| name | text_analysis |
| description | Analyze text for sentiment, entities, keywords, and linguistic patterns |
| category | nlp |
| tags | ["text","analysis","nlp","sentiment"] |
| version | 1.0.0 |
Text Analysis Skill
This skill provides natural language processing capabilities for text analysis.
When to Use
Use this skill when you need to:
- Analyze text sentiment (positive/negative/neutral)
- Extract named entities (people, places, organizations)
- Identify keywords and phrases
- Perform text classification
- Summarize long documents
Analysis Types
Sentiment Analysis
Determines the emotional tone of text:
- Positive: Expresses satisfaction, happiness, approval
- Negative: Expresses dissatisfaction, sadness, disapproval
- Neutral: Factual or informational content
Entity Extraction
Identifies and categorizes:
- People: Person names
- Places: Locations, addresses
- Organizations: Companies, institutions
- Dates: Temporal expressions
- Numbers: Quantities, measurements
Keyword Analysis
Extracts important terms and phrases:
- TF-IDF scoring
- Phrase frequency
- Collocation detection
Code Tools
@code: python analyze.py --input {input} - Perform various text analyses
Example Usage
Analyze the sentiment of this customer review: "The product exceeded my expectations!"
Result: Positive sentiment detected (confidence: 0.95)
Extract entities from: "Apple Inc. was founded by Steve Jobs in Cupertino, California."
Entities:
- Organization: Apple Inc.
- Person: Steve Jobs
- Location: Cupertino, California
