Nlp Text Analyzer

Cleanup Review

python scripts/_cache_clear.py is an optional cleanup helper for the NLP文本分析器 skill. It may recursively remove /tmp/skill_cache, /tmp/skill_scratch. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.

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：

• 中文分词（Jieba分词）
• 情感分析（SnowNLP / TextBlob）
• 关键词提取
• 文本摘要生成
• 词频统计
• 命名实体识别
• 文本分类基础
• 相似度计算
• 中英双语支持

依赖

• Python >= 3.8
• jieba >= 0.42.1
• snownlp >= 0.12.3
• textblob >= 0.17.1

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:

• Chinese tokenization (Jieba)
• Sentiment analysis (SnowNLP / TextBlob)
• Keyword extraction
• Text summarization
• Word frequency statistics
• Named entity recognition
• Text classification basics
• Similarity calculation
• Chinese/English bilingual support

Dependencies

• Python >= 3.8
• jieba >= 0.42.1
• snownlp >= 0.12.3
• textblob >= 0.17.1

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