clips/pattern
Pattern
Pattern is a Python web mining library that functions as an HTML web scraper, a natural language processing toolkit, and a network analysis tool. It provides a mathematical framework for categorizing datasets through a vector space model library.
The project enables the extraction of structured data from web services and the creation of searchable web content indexes. It processes unstructured text using sentiment analysis, part-of-speech tagging, and n-gram searching.
The library covers machine learning classification through the training of models using perceptron algorithms and support vector machines. It also includes capabilities for network graph analysis, allowing for the calculation of node centrality and the visualization of relationships between entities.
Features
- Web Data Extraction - Provides comprehensive tools for programmatically scraping and extracting structured data from web services and websites.
- Web Mining Toolkits - Combines web scraping, natural language processing, and machine learning into a single Python library for web mining.
- Natural Language Processing - Provides a comprehensive toolkit for analyzing human language through sentiment analysis, tagging, and n-gram searching.
- Text Tokenization - Includes utilities for segmenting natural language strings into contiguous n-gram sequences for linguistic analysis.
- Part-of-Speech Taggers - Ships a pipeline for assigning grammatical categories to words to enable deeper semantic text analysis.
- Web Crawling and Scraping - Ships utilities for crawling websites and analyzing HTML structures to retrieve specific data.
- Network Analysis - Calculates graph centrality and visualizes relationships between nodes in a network.
- Web Document Parsing - Provides tools to traverse website structures and convert raw HTML into structured trees for data extraction.
- Web Content Indexing - Enables the creation of searchable web content indexes by extracting and processing information from the web.
- Vector Space Models - Implements a mathematical framework for categorizing datasets using high-dimensional vector space representations.
- Web Scrapers - Provides tools for crawling websites and parsing HTML structures to extract structured data from web services.
- Classification and Clustering Models - Enables grouping of information into categories through the creation of classification and clustering models.
- Machine Learning Classification - Provides a domain for building and training models to categorize datasets using vector space algorithms.
- Model Training - Provides algorithms for training predictive models, including perceptron and support vector machine implementations.
- Perceptron Classifiers - Implements single-layer neural network architectures to classify data through iterative weight adjustments.
- Support Vector Machines - Provides support vector machine classifiers that establish decision boundaries using hyperplanes in vector space.
- Network Visualization - Includes capabilities for creating visual maps of network nodes and edges to analyze entity relationships.
- Vector Space Models - Implements a mathematical framework for categorizing datasets using vector computations and clustering algorithms.
- Network Centrality Analyses - Implements graph theory metrics like node centrality to identify influential entities within network maps.
- Natural Language Processing - Web mining module with integrated NLP and machine learning tools.
- Web Scraping - Provides high-level scraping alongside NLP and machine learning utilities.
