NLP Text Analyzer

NLP文本分析器

强大的自然语言处理工具，支持中文和英文文本分析，包含分词、情感分析、关键词提取等功能。

概述

本Skill提供完整的NLP文本分析能力：

依赖

文件结构

nlp-text-analyzer/
├── SKILL.md                  # 本文件
├── README.md                 # 使用文档
├── requirements.txt          # 依赖声明
├── scripts/
│   └── text_analyzer.py      # 文本分析脚本
├── examples/
│   └── basic_usage.py        # 使用示例
└── tests/
    └── test_nlp.py           # 单元测试

快速开始

from scripts.text_analyzer import TextAnalyzer
初始化分析器
analyzer = TextAnalyzer()
中文分词
text = "自然语言处理是人工智能的重要分支"
tokens = analyzer.segment(text)
print(tokens)
['自然语言', '处理', '是', '人工智能', '的', '重要', '分支']
情感分析
sentiment = analyzer.analyze_sentiment("这个产品真的很棒！")
print(sentiment)
{'polarity': 0.95, 'subjectivity': 0.8}
关键词提取
keywords = analyzer.extract_keywords(text, top_k=5)
print(keywords)
[('人工智能', 1.5), ('自然语言', 1.2), ...]

许可证

MIT

NLP Text Analyzer

Powerful NLP tool supporting Chinese and English text analysis, including tokenization, sentiment analysis, keyword extraction.

Overview

This Skill provides complete NLP text analysis capabilities:

Dependencies

File Structure

nlp-text-analyzer/
├── SKILL.md                  # This file
├── README.md                 # Usage documentation
├── requirements.txt          # Dependencies
├── scripts/
│   └── text_analyzer.py      # Text analysis script
├── examples/
│   └── basic_usage.py        # Usage examples
└── tests/
    └── test_nlp.py           # Unit tests

Quick Start

from scripts.text_analyzer import TextAnalyzer
Initialize analyzer
analyzer = TextAnalyzer()
Chinese tokenization
text = "Natural language processing is an important AI branch"
tokens = analyzer.segment(text)
print(tokens)
Sentiment analysis
sentiment = analyzer.analyze_sentiment("This product is really amazing!")
print(sentiment)
{'polarity': 0.95, 'subjectivity': 0.8}
Keyword extraction
keywords = analyzer.extract_keywords(text, top_k=5)
print(keywords)

License

MIT

from scripts.text_analyzer import TextAnalyzer
Initialize analyzer
analyzer = TextAnalyzer()
Chinese tokenization
text = "Natural language processing is an important AI branch"
tokens = analyzer.segment(text)
print(tokens)
Sentiment analysis
sentiment = analyzer.analyze_sentiment("This product is really amazing!")
print(sentiment)
{'polarity': 0.95, 'subjectivity': 0.8}
Keyword extraction
keywords = analyzer.extract_keywords(text, top_k=5)
print(keywords)