# clips/pattern

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Repository: https://github.com/clips/pattern
Canonical: https://ross.abutalabs.com/products/pattern
Homepage: https://github.com/clips/pattern/wiki
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: python, machine-learning, natural-language-processing, web-mining, wordnet, sentiment-analysis, network-analysis
Last push: 2026-08-05T13:05:22+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 96, release rhythm 8, longevity 100
- inputs: {"age_days": 5601, "days_push": 28, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8860, forks 1556 (observed 2026-08-28T04:10:26.064560+00:00)

## What it is
Pattern is a Python web mining module bundling tools for scraping (Google, Twitter, Wikipedia APIs, crawler, HTML DOM parser), NLP (POS tagging, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, KNN/SVM/Perceptron), and network analysis with canvas visualization. The repository is now archived and no longer maintained.

## Use cases
- scrape tweets and web pages for text mining
- run sentiment analysis on social media text
- part-of-speech tag and parse English text
- train classifiers like KNN or SVM on text vectors
- analyze and visualize network graphs
- search WordNet for word relationships

## When to choose
- you need an all-in-one classic NLP + scraping toolkit for Python 2.7/3.6
- you want a simple, well-documented library for teaching or prototyping text mining
- you need bundled sentiment analysis and WordNet access without heavy dependencies

## When to avoid
- you need maintained software with security patches and bug fixes
- you target modern Python versions (3.7+)
- you need production-grade NLP - prefer spaCy, NLTK, or scikit-learn
- you rely on live Google/Twitter APIs that have since changed

## Facets
- artifact type: library
- maturity: abandoned
- function: web-scraping, nlp, machine-learning, parser, data-visualization
- domain: machine-learning, data-science, web-development, crawlers
- platform: python, cross-platform
- tags: web-mining, wordnet, part-of-speech-tagging, text-analysis, network-analysis, archived, sentiment-analysis, natural-language-processing

## Member repositories
- clips/pattern (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:26.064560+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:24:46.320929+00:00, confidence not recorded.
  - readme: https://github.com/clips/pattern (fetched 2026-08-28T04:10:26.064560+00:00, sha da3f4248556e)
  - homepage: https://github.com/clips/pattern/wiki (fetched 2026-08-29T08:25:02.429482+00:00, sha 601b4c63a31e)
- Data as of 2026-08-30T08:39:29.467469+00:00.