Why choose between R and Python when you can have both? In this tutorial, we use a Python scraper straight from R and harness its functions for content discovery and text extraction.

Tokenization is a text segmentation process whose objective resides in dividing written text into meaningful units. This post introduces two rule-based methods to perform tokenization on German, English and beyond.

Feeds are a convenient way to get hold of a website’s most recent publications. Used alone or together with text extraction, they can allow for regular updates to databases of web documents. That way, recent documents can be collected and stored for further use. Furthermore, it can be useful to download the portions of a website programmatically to save time and resources, so that a feed-based approach is light-weight and easier to maintain.

This post describes practical ways to find recent URLs within a website and to extract text, metadata, and comments. It contains all necessary code snippets to …

This post describes practical ways to crawl websites and by working with sitemaps on the command-line. Sitemaps are particularly useful since they are made so that machines can more intelligently crawl the site. The post entails all necessary code snippets to optimize link discovery and filtering.

This post introduces two ways to validate XML documents in Python according the guidelines of the Text Encoding Initiative, using a format commonly known as TEI-XML. The first one takes a shortcut using a library I am working on, while the second one shows an exhaustive way to perform the operation.

Both ground on LXML, an efficient library for processing XML and HTML. The following lines of code will try to parse and validate a document in the same directory as the terminal window or Python console.

Shortcut with the trafilatura library

I am currently using this web scraping …

Trafilatura is a Python library designed to download, parse, and scrape web page data. It also offers tools that can easily help with website navigation and extraction of links from sitemaps and feeds.

Description

Metadata extraction

Diverse content extraction and scraping techniques are routinely used on web document collections by companies and research institutions alike. Being able to better qualify the contents allows for insights based on metadata (e.g. content type, authors or categories), better bandwidth control (e.g. by knowing when webpages have been updated), or optimization of indexing (e.g. language-based heuristics, LRU cache, etc.).

In short, metadata extraction is useful for different kinds of purposes ranging from knowledge extraction and business intelligence to classification and refined visualizations. It is often necessary to fully parse the document or apply robust …

I would like to introduce the way I clean lists of unknown URLs before going further (e.g. by retrieving the documents). I often use a Python script named clean_urls.py which I made available under a open-source license as a part of the FLUX-toolchain.

The following Python-based regular expressions show how malformed URLs, URLs leading to irrelevant content as well as URLs which obviously lead to adult content and spam can be filtered using a rule-based approach.

Avoid recurrent sites and patterns to save bandwidth

First, it can be useful to make sure that the URL was properly …

As I recently tried several modeling techniques in R, I would like to share some of these, with a focus on linear regression.

Disclaimer: the code lines below work, but I would not suggest that they are the most efficient way to deal with this kind of data (as a matter of fact, all of them score slightly below 80% accuracy on the Kaggle datasets). Moreover, there are not always the most efficient way to implement a given model.

I see it as a way to quickly test several frameworks without going into details.

The column names used in the …

Let’s pretend you recently installed R (a software to do statistical computing), you have a text collection you would like to analyze or classify and some time to lose. Here are a few quick commands that could get you a little further. I also write this kind of cheat sheet in order to remember a set of useful tricks and packages I recently gathered and from which I thought they could help others too.

Letter frequencies

In this example I will use a series of characteristics (or features) extracted from a text collection, more precisely the frequency of each …