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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 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.
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.
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.