Pattern is a comprehensive Python module for web mining, offering integrated tools for data mining, natural language processing, machine learning, and network analysis. It targets developers and researchers needing to extract, process, and analyze data from the web, providing a unified toolkit for complex data-driven tasks.

How It Works

Pattern employs a modular design, integrating various algorithms and data structures for its diverse functionalities. For NLP, it includes a Brill tagger and sentiment analysis. Machine learning is supported by vector space models and classifiers like KNN and SVM. Network analysis leverages graph centrality algorithms and visualization tools. This integrated approach simplifies complex workflows by providing a single, well-documented library for multiple data science tasks.

Quick Start & Requirements

• Install via pip: pip install pattern
• Supports Python 2.7 and Python 3.6.
• Bundled with dependencies like Brill tagger, LIBSVM, LIBLINEAR, and NetworkX.
• Official documentation and examples are available.

Highlighted Details

• Offers tools for web scraping, HTML DOM parsing, and web service integration (Google, Twitter, Wikipedia).
• Includes advanced NLP capabilities such as part-of-speech tagging, sentiment analysis, and WordNet integration.
• Provides machine learning algorithms including vector space models, clustering, and classification (KNN, SVM, Perceptron).
• Features network analysis tools for graph centrality and visualization.

Maintenance & Community

The project is hosted on GitHub and welcomes contributions and donations. Key contributors include Tom De Smedt and Walter Daelemans.

Licensing & Compatibility

Licensed under BSD, allowing for commercial use and integration into closed-source projects.

Limitations & Caveats

The project officially supports Python 2.7 and 3.6, with no explicit mention of compatibility with newer Python versions.