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In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section.
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.
When using all user tweets, they reached an accuracy of 88.0%.
An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network. When adding more information sources, such as profile fields, they reach an accuracy of 92.0%.
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.
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.
Their features were hash tags, token unigrams and psychometric measurements provided by the Linguistic Inquiry of Word Count software (LIWC; (Pennebaker et al. Although LIWC appears a very interesting addition, it hardly adds anything to the classification.
For gender, the system checks the profile for about 150 common male and 150 common female first names, as well as for gender related words, such as father, mother, wife and husband.
If no cue is found in a user s profile, no gender is assigned.
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.
The age component of the system is described in (Nguyen et al. The authors apply logistic and linear regression on counts of token unigrams occurring at least 10 times in their corpus.