Speed dating amsterdam hoger opgeleiden uniformedating com
Later, in 2004, the group collected a Blog Authorship Corpus (BAC; (Schler et al.
2006)), containing about 700,000 posts to (in total about 140 million words) by almost 20,000 bloggers. Slightly more information seems to be coming from content (75.1% accuracy) than from style (72.0% accuracy). We see the women focusing on personal matters, leading to important content words like love and boyfriend, and important style words like I and other personal pronouns.
2009) managed to increase the gender recognition quality to 89.2%, using sentence length, 35 non-dictionary words, and 52 slang words.
The authors do not report the set of slang words, but the non-dictionary words appear to be more related to style than to content, showing that purely linguistic behaviour can contribute information for gender recognition as well.
Two other machine learning systems, Linguistic Profiling and Ti MBL, come close to this result, at least when the input is first preprocessed with PCA. Introduction In the Netherlands, we have a rather unique resource in the form of the Twi NL data set: a daily updated collection that probably contains at least 30% of the Dutch public tweet production since 2011 (Tjong Kim Sang and van den Bosch 2013).
One gets the impression that gender recognition is more sociological than linguistic, showing what women and men were blogging about back in A later study (Goswami et al.
In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields.
And, obviously, it is unknown to which degree the information that is present is true.
172 For Tweets in Dutch, we first look at the official user interface for the Twi NL data set, Among other things, it shows gender and age statistics for the users producing the tweets found for user specified searches.
These statistics are derived from the users profile information by way of some heuristics.