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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.You'll find complete galleries of all the samples above in our members section, together with much much more.But moments after Carlotta Ferlito finished fifth, she told a journalist that she had joked with teammate Vanessa Ferrari, who come fourth, that they should paint themselves black next time to stand a chance of winning. But, really the starting score, I understand since I didn't connect, but the execution, I don't know, I will ask, we'll see., he said: 'Carlotta was referring to a trend in gymnastics at this moment, which is going towards a technique that opens up new chances to athletes of colour (well-known for power) while penalising the more artistic Eastern European style that allowed Russians and Romanians to dominate the sport for years.' Bitter: Italian Carlotta Ferlito, left, made her gibe after finishing in fifth place behind American Simone Biles, right, who became the sport's first black world champion with victory in the all-round competition He apparently added: 'Why are there no black swimmers?And, obviously, it is unknown to which degree the information that is present is true.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 metadata, user relations, profile photos, and the text of the tweets.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.In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section.
Two other machine learning systems, Linguistic Profiling and Ti MBL, come close to this result, at least when the input is first preprocessed with PCA. Introduction In the Netherlands, we have a rather unique resource in the form of the Twi NL 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).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.2004), with and without preprocessing the input vectors with Principal Component Analysis (PCA; (Pearson 1901); (Hotelling 1933)).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.Their highest score when using just text features was 75.5%, testing on all the tweets by each author (with a train set of 3.3 million tweets and a test set of about 418,000 tweets). (2012) used SVMlight to classify gender on Nigerian twitter accounts, with tweets in English, with a minimum of 50 tweets.