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Pattern is a web mining module for the Python programming language.
Web Scraping Tools
Data mining tools Natural language processing Network analysis Machine learning
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
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It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and
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Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and visualization. The pattern.web module is a web toolkit that contains API's (Google, Gmail, Bing, Twitter, Facebook, Wikipedia, Wiktionary, DBPedia, Flickr, ...), a robust HTML DOM parser and a web crawler.
The pattern.en module is a natural language processing (NLP) toolkit for English. Because language is ambiguous (e.g., I can ↔ a can) it uses statistical approaches + regular expressions. This means that it is fast, quite accurate and occasionally incorrect. It has a part-of-speech tagger that identifies word types (e.g., noun, verb, adjective), word inflection (conjugation, singularization) and a WordNet API.
The pattern.search module contains a search algorithm to retrieve sequences of words (called n-grams) from tagged text. The search pattern NP be RB?+ important than NP means any noun phrase (NP) followed by the verb to be, followed by zero or more adverbs (RB, e.g., much, more), followed by the words important than, followed by any noun phrase.
It will also match "The mobile web will be much less important than mobile apps" and other grammatical variations. The pattern.vector module is a toolkit for machine learning, based on a vector space model of bag-of-words documents with weighted features (e.g., tf-idf) and distance metrics (e.g., cosine similarity, infogain). Models can be used for clustering (k-means, hierarchical), classification (Naive Bayes, Perceptron, k-NN, SVM) and latent semantic analysis (LSA).
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