Pass every single HTML page to Trafilatura to parse the text content.Extract all of the HTML content using requests into a python dictionary.This is solely because this tutorial is written in a Jupyter Notebook.įirstly we’ll break the problem down into several stages: NB: If you’re writing this in a standard python file, you won’t need to include the ! symbol. In this article you’ll learn how to extract the text content from single and multiple web pages using Python. That’s it, feel free to share it, use it, and contribute if you feel you can make this module better.When performing content analysis at scale, you’ll need to automatically extract text content from web pages. You can of course increase this recall by adding more patterns to the HTML extraction function, but you are risking in a lower precision score. We tested the “Article Date Extractor” module against Google’s news feed, and got close to 100% precision with almost 90% recall. For the JSON-LD part, we use the built in JSON module, to load and parse the JSON. It has a powerful parsing capabilities, and it’s very simple to use. In order to parse the HTML document, we use Beautiful Soup. It’s an amazing solution that converts textual date, into a datetime object. Once we find the textual date, we unify it using the excellent python-dateutil module. A mixed of standards and popular date annotations are evaluated in order to find the elusive date: With the risk of loosing accuracy, if all fails we look into the HTML. There are many types of meta tags (a lots of standards remember?) so we try to go over all of the different formats. If JSON-LD fails (it usually does), we try to look in the document’s meta tags for the date. Some documents specify the creation or publication date using this methods, it’s always worth a try! JSON-LD is an easy-to-use JSON-based linked data format that defines the concept of context to specify the vocabulary for types and properties. We use a regular expression to try and match against multiple formats (, 1-1-2015, ,1_1_2015). More often than not the date exists in the URL of the post, but since it doesn’t include the time, we try to extract it as a fallback, in case other methods fail. Unfortunately, there are A LOT of standards! The date extraction function tries multiple methods to accurately extract and normalize the date. Not to mention that there could be multiple types of separators and the following date,, can be interpreted as January 2nd, or February 1st (depending if it’s the American or European format).įortunately there are standards out there. It can be based on a numerical format (i.e ), a textual format (i.e Yesterday), or even a combination of them both (Jan 1st, 2015). A publication date can appear in various ways and multiple formats. Here at Webz.io we use multiple methods to automatically detect and extract the date out of articles, blog posts and comments. There are some commercial solutions out there, but why not just use this module for free? A few days ago I’ve released an open source Python module that provides you with a simple way to extract and normalize the publication date of any online blog or news post.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |