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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.STE Preisvergleiche für DSL Smartphone & Handy Strom Gas Hotel Tarifvergleiche Willst du Lachen, Camen, lustig Chatten!? Code mit Handy oder Smartphone einscannen und Link ? Camli Türk sohbet odalari Türk camli chat, sohbet odalari, Türk? However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata.
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.
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.
For each blogger, metadata is present, including the blogger s self-provided gender, age, industry and astrological sign. The creators themselves used it for various classification tasks, including gender recognition (Koppel et al. 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.
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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.