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Fast and robust extraction of original and updated publication dates from URLs and web pages.

Project description

Python package Python versions Documentation Status Code Coverage Downloads

Code:https://github.com/adbar/htmldate
Documentation:https://htmldate.readthedocs.io
Issue tracker:https://github.com/adbar/htmldate/issues

Find original and updated publication dates of any web page. From the command-line or within Python, all the steps needed from web page download to HTML parsing, scraping, and text analysis are included.

In a nutshell


Demo as GIF image

With Python:

>>> from htmldate import find_date
>>> find_date('http://blog.python.org/2016/12/python-360-is-now-available.html')
'2016-12-23'
>>> find_date('https://netzpolitik.org/2016/die-cider-connection-abmahnungen-gegen-nutzer-von-creative-commons-bildern/', original_date=True)
'2016-06-23'

On the command-line:

$ htmldate -u http://blog.python.org/2016/12/python-360-is-now-available.html
'2016-12-23'

Features

  • Compatible with all recent versions of Python (see above)
  • Multilingual, robust and efficient (used in production on millions of documents)
  • URLs, HTML files, or HTML trees are given as input (includes batch processing)
  • Output as string in any date format (defaults to ISO 8601 YMD)
  • Detection of both original and updated dates

htmldate finds original and updated publication dates of web pages using heuristics on HTML code and linguistic patterns. It provides following ways to date an HTML document:

  1. Markup in header: Common patterns are used to identify relevant elements (e.g. link and meta elements) including Open Graph protocol attributes and a large number of CMS idiosyncrasies
  2. HTML code: The whole document is then searched for structural markers: abbr and time elements as well as a series of attributes (e.g. postmetadata)
  3. Bare HTML content: A series of heuristics is run on text and markup:
  • in fast mode the HTML page is cleaned and precise patterns are targeted
  • in extensive mode all potential dates are collected and a disambiguation algorithm determines the best one

Performance

500 web pages containing identifiable dates (as of 2022-03-23 on Python 3.8)
Python Package Precision Recall Accuracy F-Score Time
articleDateExtractor 0.20 0.769 0.691 0.572 0.728 4.4x
date_guesser 2.1.4 0.738 0.544 0.456 0.626 17x
goose3 3.1.11 0.821 0.453 0.412 0.584 15x
htmldate[all] 1.2.1 (fast) 0.848 0.921 0.790 0.883 1x
htmldate[all] 1.2.1 (extensive) 0.839 0.990 0.832 0.908 2.3x
newspaper3k 0.2.8 0.729 0.630 0.510 0.675 12x
news-please 1.5.21 0.769 0.691 0.572 0.728 40x

For complete results and explanations see the evaluation page.

Installation

This Python package is tested on Linux, macOS and Windows systems; it is compatible with Python 3.6 upwards. It is available on the package repository PyPI and can notably be installed with pip (pip3 where applicable): pip install htmldate and optionally pip install htmldate[speed].

Documentation

For more details on installation, Python & CLI usage, please refer to the documentation: htmldate.readthedocs.io

License

htmldate is distributed under the GNU General Public License v3.0. If you wish to redistribute this library but feel bounded by the license conditions please try interacting at arms length, multi-licensing with compatible licenses, or contacting me.

See also GPL and free software licensing: What’s in it for business?

Author

This effort is part of methods to derive information from web documents in order to build text databases for research (chiefly linguistic analysis and natural language processing). Extracting and pre-processing web texts to the exacting standards of scientific research presents a substantial challenge for those who conduct such research. There are web pages for which neither the URL nor the server response provide a reliable way to find out when a document was published or modified. For more information:

JOSS article Zenodo archive
@article{barbaresi-2020-htmldate,
  title = {{htmldate: A Python package to extract publication dates from web pages}},
  author = "Barbaresi, Adrien",
  journal = "Journal of Open Source Software",
  volume = 5,
  number = 51,
  pages = 2439,
  url = {https://doi.org/10.21105/joss.02439},
  publisher = {The Open Journal},
  year = 2020,
}

You can contact me via my contact page or GitHub.

Contributing

Contributions are welcome!

Feel free to file issues on the dedicated page. Thanks to the contributors who submitted features and bugfixes!

Kudos to the following software libraries:

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