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With lexical N-grams, they reached an accuracy of 67.7%, which the combination with the sociolinguistic features increased to 72.33%.(2011) attempted to recognize gender in tweets from a whole set of languages, using word and character N-grams as features for machine learning with Support Vector Machines (SVM), Naive Bayes and Balanced Winnow2.Frequent redesigns keep them on top of latest communication trends and member needs.Gaydar also includes Gaydar Radio for entertainment while you browse.The paper does not describe the gender component, but the first author has informed us that the accuracy of the gender recognition on the basis of 200 tweets is about 87% (Nguyen, personal communication).

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on this, we will still take the biological gender as the gold standard in this paper, as our eventual goal is creating metadata for the Twi NL collection.

Experimental Data and Evaluation In this section, we first describe the corpus that we used in our experiments (Section 3.1).2004), with and without preprocessing the input vectors with Principal Component Analysis (PCA; (Pearson 1901); (Hotelling 1933)).

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