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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.
2004), with and without preprocessing the input vectors with Principal Component Analysis (PCA; (Pearson 1901); (Hotelling 1933)).
We also varied the recognition features provided to the techniques, using both character and token n-grams.
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.
A group which is very active in studying gender recognition (among other traits) on the basis of text is that around Moshe Koppel. 2002) they report gender recognition on formal written texts taken from the British National Corpus (and also give a good overview of previous work), reaching about 80% correct attributions using function words and parts of speech.Wenn du auf unsere Webseite klickst oder hier navigierst, stimmst du der Erfassung von Informationen durch Cookies auf und außerhalb von Facebook zu.Weitere Informationen zu unseren Cookies und dazu, wie du die Kontrolle darüber behältst, findest du hier: Cookie-Richtlinie.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).However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata.
For all techniques and features, we ran the same 5-fold cross-validation experiments in order to determine how well they could be used to distinguish between male and female authors of tweets.