- name
- nlp-text-analyzer
- description
- NLP文本分析器 - 支持分词、情感分析、关键词提取、文本分类等自然语言处理功能 | NLP Text Analyzer - Tokenization, sentiment analysis, keyword extraction, text classification
- homepage
- https://github.com/kaiyuelv/nlp-text-analyzer
- category
- nlp
- tags
- version
- 1.0.0
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 testsQuick 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