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They used lexical features, and present a very good breakdown of various word types. We used the most frequent, as measured on our tweet collection, of which the example tweet contains the words ik, dat, heeft, op, een, voor, and het.

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Figure 5 shows all token unigrams. This means that the content of the n-grams is more important than their form. Finally, as the use of capitalization and diacritics is quite haphazard in the tweets, the tokenizer strips all words of diacritics and transforms them to lower case.

However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata.

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Top Function Words The most frequent function words see kestemont for an overview. On the female side, we see a representation of the world of the prototypical young female Twitter user.

They report an overall accuracy of In this case, it would seem that the systems are thrown off by the political texts. As the separation value and the percentages are generally correlated, Free online dating wien bigger tokens are found further away from the diagonal, while the area close to the diagonal contains mostly unimportant and therefore unreadable tokens.

SVR tends to place him clearly in the male area with all the feature types, with unigrams at the extreme with a score of SVR with PCA on the other hand, is less convinced, and even classifies him as female for unigrams 1.

Again, we take the token unigrams as a starting point. The exception also leads to more varied classification by the different systems, yielding a wide range of scores.

Furthermore, LP appears to suffer some kind of mathematical breakdown for higher numbers of components. Even so, there are circumstances where outright recognition is not an option, but where one must be content with profiling, i. The only hyperparameters we varied in the grid search are the metric Numerical and Cosine distance and the weighting no weighting, information gain, gain ratio, chi-square, shared variance, and standard deviation.

Gender Recognition on Dutch Tweets - PDF

Taking again SVR on unigrams as our starting point, this group contains 11 males and 16 females. For the measurements with PCA, the number of principal components provided to the classification system is learned from the development data.

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After this, we examine the classification of individual authors Section 5. The creators themselves used it for various classification tasks, including gender recognition Koppel et al. This type of character n-gram has the clear advantage of not needing any preprocessing in the form of tokenization.

But it might alsomean that the gender just influences all feature types to a similar degree. In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields.

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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Feature type Unigram Bigram Trigram Skipgram Char 5-gram Top Function 14 get the impression that Dutch is not his native language, which is supported by his name. Identity disclosed with permission. Another interesting group of authors is formed by the misclassified ones. With these main choices, we performed a grid search for well-performing hyperparameters, with the following investigated values: Finally, we included feature types based on character n-grams following kjell et al.

Starting with the systems, we see that SVR using original vectors consistently outperforms the other two.

Skip bigrams Two tokens in the tweet, but not adjacent, without any restrictions on the gap size. For each blogger, metadata is present, including the blogger s self-provided gender, age, industry and astrological sign.

This has also been remarked by Bamman et al. Original 4-gram About K features. In the example tweet, we find e. Normalized 3-gram About 36K features. We will focus on the token n-grams and the normalized character 5-grams. With one exception author is recognized as male when using trigramsall feature types agree on the misclassification.