CRAN - Package tm

Who is Satoshi Nakamoto? Bitcoin’s Mysterious Creator Revealed as Nick Szabo - IBTimes UK
▻http://www.ibtimes.co.uk/articles/527078/20131203/satoshi-nakamoto-nick-szabo-bitcoin-creator-revealed.htm

There is very little information about Szabo available on the internet, except for his two blogs, where he wirtes in depth on issues as diverse as history, law and economics.

The complete lack of details about his professional career, particularly about his time as a law and economics professor at George Washington University is odd in this day and age - but it could just be that Szabo values his privacy. 

On a Reddit thread, some people have pointed out flaws in the blogger’s theory, including the fact the approach used has not been validated on an dataset.

The fact the author of this theory also wants to remain anonymous adds a further level of mystery to the bitcoin enigma, as well as possibly weakening the credibility of the thesis.

Il ne dit pas quels outils il utilise pour cette analyse. Tu sais comment ça se bricole ça ? Il y a un projet sur lequel j’aimerais bien faire la même chose.

non, mais ça m’intéresse :)

peut-être comme Shakespeare : ▻http://blogs.plos.org/everyone/2013/08/29/a-way-with-words-data-mining-uncloaks-authors-stylistic-flair
▻http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0066813

Non, il ne dit pas, mais vu qu’il ne fait que des comptages, n’importe quel outil de statistique textuelle devrait faire l’affaire.

Par exemple dans R, le package tm ▻http://cran.r-project.org/web/packages/tm/index.html

La présentation du package ci-dessous commence par une rapide revue de logiciels disponibles (commerciaux et open source )
▻http://www.jstatsoft.org/v25/i05

Il y a aussi une interface graphique au dessus de tm : R.TeMiS
▻http://mediacorpus.hypotheses.org/104

(Note : aussi bien pour l’analyse textuelle que pour R, je sais juste que ça existe…)

Should We Teach Literature Students How To Analyze Texts Algorithmically? ⚙ Co.Labs ⚙ code + community
▻http://www.fastcolabs.com/3016699/should-we-teach-literature-students-how-to-analyze-texts-algorithmically

Reverse Engineering J.K. Rowling

To explore that question, we should first look at how the Juola cracked Rowling’s writing style. To begin the process, Juola loaded 1,000-word samples of The Cuckoo’s Calling in to his self-designed Java Graphical Authorship Attribution Program (JGAAP), along with several other texts, including The Casual Vacancy, Rowling’s first post-Harry Potter novel. A freely available Java-based, modular program for textual analysis, categorization, and authorship attribution, JGAAP analyzed the texts on four different variables: word-length distribution, the use of common words like “the” and “of,” recurring-word pairings, and the distribution of “character 4-grams,” or groups of four adjacent characters, words, or parts of words. The computer analysis took around 30 minutes in total.

#n-gram