The Microblog Explorer project is about gathering URLs from social networks (FriendFeed, identi.ca, and Reddit) to use them as web crawling seeds. At least by the last two of them a crawl appears to be manageable in terms of both API accessibility and corpus size, which is not the case concerning Twitter for example.

Hypotheses:

1. These platforms account for a relative diversity of user profiles.
2. Documents that are most likely to be important are being shared.
3. It becomes possible to cover languages which are more rarely seen on the Internet, below the English-speaking spammer’s radar.
4. Microblogging services are …

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 …

Introduction

I already described how to build a basic specialized crawler on this blog. I also wrote about crawling a newspaper website to build a corpus. As I went on work on this issue, I decided to release a few useful scripts under an open-source license.

The crawlers are not just mere link-harvesters, they are designed to be used as corpus-builders. As one cannot republish anything but quotations of the texts, the purpose is to enable others to make their own version of the corpora. Since the newspapers are updated quite often, it is not imaginable to create exact duplicates …

As I try to put my notes in order by the end of this year, I changed a series of references, most notably in the bibliography and in the links sections.

Bibliography

I just updated the bibliography, using new categories. I divided the references in two main sections:

Corpus Linguistics, Complexity and Readability Assessment

• Corpus Linguistics and Corpus Building
• Complexity and Linguistic Complexity Theory
• Text Comprehension and Readability Theory
• Readability Formulas
• Readability Assessment

Background

• General Linguistics
• Computational Linguistics
• Measurement Theory
• Philosophy of Language
• Philosophy of Technology
• Miscellaneous Tools
• Tagging and Marking

Links

First of all, I updated the links section …

I am currently working on a resource I would like to introduce : the German Political Speeches Corpus (no acronym apart from GPS). It consists in speeches by the last German Presidents and Chancellors as well as a few ministers, all gathered from official sources.

As far I as know no such corpus was publicly available for German. Most speeches could not be found on Google until today (which is bound to change). It can be freely republished.

The two main corpora (Presidency and Chancellery) are released in XML format basing on raw text and metadata.

There is a series of …

Basing on my previous post about specialized crawlers, I will show how I to crawl a French sports newspaper named L’Equipe using scripts written in Perl, which I did lately. For educational purpose, it works by now but it is bound to stop being efficient as soon as the design of the website changes.

Gathering links

First of all, you have to make a list of links so that you have something to start from. Here is the beginning of the script:

#!/usr/bin/perl #assuming you're using a UNIX-based system...
use strict; #because it gets messy without …

Given a dictionary, say one of the frequent words lists of the University of Leipzig, given a series of words: How can you check which ones belong to the list ?

Another option would be to use the operator available since Perl 5.10: :::perl if ($word ~~ @list) {…} But this gets very slow if the size of the list increases. I wrote a naive implementation of the binary search algorithm in Perl that I would like to share. It is not that fast though. Basic but it works.

First of all the wordlist gets read:

my $dict = 'leipzig10000';
open (DICTIONARY, $dict …