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In the following sections, we first present some previous work on gender recognition Section 2. Gender recognition has also already been applied to Tweets.

With lexical N-grams, they reached an accuracy of The male which is attributed the most female score is author All systems have no trouble recognizing him as a male, with the lowest scores around 1 for the top function words. The authors apply logistic and linear regression on counts of token unigrams occurring at least 10 times in their corpus.

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For the unigrams, SVR reaches its peak Then we will focus on the effect of preprocessing the input vectors with PCA Section 5. In effect, this N is a further hyperparameter, which we varied from 1 to the total number of components usuallyas there are authorsusing a stepsize of 1 from 1 to 10, and then slowly increasing the stepsize to a maximum of 20 when over The age is reconfirmed by the endearingly high presence of mama and papa.

From the aboutusers who are assigned a gender by TwiQS, we took a random selection in such a manner that the volume distribution i.

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Accuracy Percentages for various Feature Types and Techniques. On re examination, we see a clearly male first name and also profile photo. For each test author, we determined the optimal hyperparameter settings with regard to the classification of all other authors in the same part of the corpus, in effect using these as development material.

LP keeps its peak at 10, but now even lower than for the token n-grams However, we used two types of character n-grams. Results In this section, we will present the overall results of the gender recognition.

If we search for the word parlement parliament in our corpus, which is used 40 times by Sargentini, we find two more female authors each using it onceas compared to 21 male authors with up to 9 uses.

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Are they mostly targeting the content of the tweets, i. The word haar may be the pronoun her, but just as well the noun hair, and in both cases it is actually more related to the Several errors could be traced back to the fact that the account had moved on to another user since We could have used different dividing strategies, but chose balanced folds in order to give a equal chance to all machine learning techniques, also those that have trouble with unbalanced data.

In the example tweet, e. Then follow the results Section 5and Section 6 concludes the paper. However, looking at SVR is not an option here. Finally, we included feature types based on character n-grams following kjell et al. And, obviously, it is unknown to which degree the information that is present is true.