Simplified python article discovery & extraction.
Inspired by requests for its simplicity and powered by lxml for its speed; newspaper is a Python 2 library for extracting & curating articles from the web.
Newspaper utilizes lxml and caching for speed. Also, everything is in unicode
>>> import newspaper >>> cnn_paper = newspaper.build('http://cnn.com') # ~15 seconds >>> for article in cnn_paper.articles: >>> print article.url # filters to only valid news urls u'http://www.cnn.com/2013/11/27/justice/tucson-arizona-captive-girls/' u'http://www.cnn.com/2013/12/11/us/texas-teen-dwi-wreck/index.html' u'http://www.cnn.com/2013/12/07/us/life-pearl-harbor/' ... >>> print cnn_paper.size() # number of articles 3100 >>> print cnn_paper.category_urls() [u'http://lifestyle.cnn.com', u'http://cnn.com/world', u'http://tech.cnn.com' ...] >>> print cnn_paper.feed_urls() [u'http://rss.cnn.com/rss/cnn_crime.rss', u'http://rss.cnn.com/rss/cnn_tech.rss', ...] # ^ categories and feeds are cached for a day (adjustable) # ^ searches entire cnn sitemap to find the feeds, not just homepage #### build articles, then download, parse, and perform NLP >>> for article in cnn_paper.articles[:5]: >>> article.download() # take's a while if you're downloading 1K+ articles >>> print cnn_paper.articles.html u'<!DOCTYPE HTML><html itemscope itemtype="http://...' # won't work, we only downloaded 5 articles >>> print cnn_paper.articles.html u'' ### parse an article for it's text, authors, etc >>> first_article = cnn_paper.articles >>> first_article.parse() >>> print first_article.text u'Three sisters who were imprisoned for possibly...' >>> print first_article.top_img u'http://some.cdn.com/3424hfd4565sdfgdg436/ >>> print first_article.authors [u'Eliott C. McLaughlin', u'Some CoAuthor'] >>> print first_article.title u'Police: 3 sisters imprisoned in Tucson home' #### extract nlp (must be on an already parsed article >>> first_article.nlp() >>> print first_article.summary u'...imprisoned for possibly a constant barrage...' >>> print first_article.keywords [u'music', u'Tucson', ... ] # now try nlp() on an article that hasen't been downloaded >>> print cnn_paper.articles.nlp() Traceback (... ... ArticleException: You must parse an article before you try to.. #### some other news-source level functionality >>> print cnn_paper.brand u'cnn' >>> print cnn_paper.description u'CNN.com delivers the latest breaking news and information on the latest...'
Unless told not to in the constructor via the is_memo_articles param (default true), newspaper automatically caches all category, feed, and article urls. This is both to avoid duplicate articles and for speed.
Suppose the above code has already been run on the cnn domain once. Previous article urls are cached and dupes are removed so we only get new articles. >>> import newspaper >>> cnn_paper = newspaper.build('http://cnn.com') >>> cnn_paper.size() 60 # indicates that since we last ran build(), cnn has published 60 new articles! # If you'd like to opt out of memoization, init your newspapers with >>> cnn_paper2 = newspaper.build('http://cnn.com', is_memo=False) >>> cnn_paper2.size() 3100
Alternatively, you may use newspaper’s lower level Article api.
>>> from newspaper import Article >>> article = Article('http://cnn.com/2013/11/27/travel/weather-thanksgiving/index.html') >>> article.download() >>> print article.html u'<!DOCTYPE HTML><html itemscope itemtype="http://...' >>> article.parse() >>> print article.text u'The purpose of this article is to introduce...' >>> print article.authors [u'Martha Stewart', u'Bob Smith'] >>> print article.top_img u'http://some.cdn.com/3424hfd4565sdfgdg436/ >>> print article.title u'Thanksgiving Weather Guide Travel ...' >>> article.nlp() >>> print article.summary u'...and so that's how a Thanksgiving meal is cooked...' >>> print article.keywords [u'Thanksgiving', u'holliday', u'Walmart', ...]
nlp() is expensive, as is parse(), make sure you actually need them before calling them on all of your articles! In some cases, if you just need urls, even download() is not necessary.
Sorry for the sloppy set-up so far, this is my first real package uploaded to pip i’m trying to fix the dist!
- News url identification
- Text extraction from html
- Keyword extraction from text
- Summary extraction from text
- Author extraction from text
- Top Image & All image extraction from html
- Top Google trending terms
- News article extraction from news domain
- Quick html downloads via multithreading
Get it now
$ pip install newspaper $ curl https://raw.github.com/codelucas/newspaper/master/download_corpora.py | python
See more examples at the Quickstart guide.
Full documentation is available at Quickstart guide.
- Python >= 2.6 and <= 2.7*
MIT licensed. Also, view the LICENSE for our internally used libraries at: goose-license
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