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Then we describe our experimental data and the evaluation method (Section 3), after which we proceed to describe the various author profiling strategies that we investigated (Section 4). Gender Recognition Gender recognition is a subtask in the general field of authorship recognition and profiling, which has reached maturity in the last decades(for an overview, see e.g. Even so, there are circumstances where outright recognition is not an option, but where one must be content with profiling, i.e.Then follow the results (Section 5), and Section 6 concludes the paper. For whom we already know that they are an individual person rather than, say, a husband and wife couple or a board of editors for an official Twitterfeed. the identification of author traits like gender, age and geographical background.The general quality of the assignment is unknown, but in the (for this purpose) rather unrepresentative sample of users we considered for our own gender assignment corpus (see below), we find that about 44% of the users are assigned a gender, which is correct in about 87% of the cases.Another system that predicts the gender for Dutch Twitter users is Tweet Genie ( that one can provide with a Twitter user name, after which the gender and age are estimated, based on the user s last 200 tweets.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.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.
These statistics are derived from the users profile information by way of some heuristics.For our experiment, we selected 600 authors for whom we were able to determine with a high degree of certainty a) that they were human individuals and b) what gender they were.We then experimented with several author profiling techniques, namely Support Vector Regression (as provided by LIBSVM; (Chang and Lin 2011)), Linguistic Profiling (LP; (van Halteren 2004)), and Ti MBL (Daelemans et al.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.
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).