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However, even style appears to mirror content. 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. The men, on the other hand, seem to be more interested in computers, leading to important content words like software and game, and correspondingly more determiners and prepositions. 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. 2009) managed to increase the gender recognition quality.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. Gender recognition has also already been applied to Tweets. (2010) examined various traits of authors from India tweeting in English, combining character N-grams and sociolinguistic features like manner of laughing, honorifics, and smiley use.

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. In (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 pigmentvlekken 80 correct attributions using function words and parts of speech. Later, in 2004, the group collected a blog Authorship Corpus (BAC; (Schler. 2006 containing about 700,000 posts to m (in total about 140 million words) by almost 20,000 bloggers. For each blogger, metadata is present, including the blogger s self-provided gender, age, industry and astrological sign. This corpus has been used extensively since. The creators themselves used it for various classification tasks, including gender recognition (Koppel. They report an overall accuracy.1. Slightly more information seems to be coming from content (75.1 accuracy) than from style (72.0 accuracy).

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investigated (Section 4). 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. C 2014 van Halteren and Speerstra. 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. (Juola 2008) and (Koppel. Currently the field is getting an impulse for further development now that vast data sets of user generated data is becoming available. (2012) show that authorship recognition is also possible (to some degree) if the number of candidate authors is as high as 100,000 (as compared to the usually less than ten in traditional studies). Even so, there are circumstances where outright recognition is not an option, but where one must be content with profiling,. The identification of author traits like gender, age and geographical background.
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Gender Recognition on Dutch

The resource would become even more useful if we could deduce complete and correct metadata from the various available information sources, such as the provided speedtest metadata, user relations, profile photos, and the text of the tweets. In this paper, we start modestly, by attempting to derive just the gender of the authors 1 automatically, purely on the basis of the content of their tweets, using author profiling techniques. 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 timbl (Daelemans. 2004 with and without preprocessing the input vectors with Principal Component venusheuvel Analysis (PCA; (Pearson 1901 (Hotelling 1933). We also varied the recognition features provided to the techniques, using both character and token n-grams. 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.

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1 Computational Linguistics in the netherlands journal 4 (2014) Submitted 06/2014; Published 12/2014 Gender Recognition on Dutch Tweets Hans van Halteren Nander Speerstra radboud University nijmegen, cls, linguistics Abstract In this paper, we investigate gender recognition on Dutch Twitter material, using a corpus consisting. We achieved the best results,.5 correct assignment in a 5-fold cross-validation on our corpus, with Support Vector Regression on all token unigrams. Two other machine learning systems, linguistic Profiling and timbl, come close to this result, at least when the input is geschoren first preprocessed with pca. Introduction In the netherlands, we have a rather unique resource in the form of the Twinl 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. 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.

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