Welke vragen stellen bij online dating. Gender recognition on dutch tweets - pdf

This number was treated as just another hyperparameter to be selected. Rather than using fixed hyperparameters, we let the control shell choose them automatically in a grid search procedure, based on development data. When you share, everyone wins. In order to improve the robustness of the hyperparameter selection, the best three settings were chosen and used for classifying the current author in question.

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The male which is attributed the most female score is author For SVR, one would expect symmetry, as both classes are modeled simultaneously, and differ merely in the sign of the numeric class identifier.

Top Function Words The most frequent function words see kestemont for an overview. However, looking at SVR is not an option here.

And LP just mirrors its behaviour with unigrams. The best recognizable female, authoris not as focused as her male counterpart.

For the unigrams, SVR reaches its peak The position in the plot represents the relative number of men and women who used the token at least once somewhere in their tweets. Figures 1, 2, and 3 show accuracy measurements for the token unigrams, token bigrams, and normalized character 5-grams, for all three systems at various numbers of principal components.

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.

Passing Messages

Our primary choice for classification was the use of Support Vector Machines, viz. And by TweetGenie as well.

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The conclusion is not so much, however, that humans are also not perfect at guessing age on the basis of language use, but rather that there is a distinction between the biological and the social identity of authors, and language use is more likely to represent the social one cf.

A group which is very active in studying gender recognition among other traits on the basis of text is that around Moshe Koppel. This is in accordance with the hypothesis just suggested for the token n-grams, as normalization too brings the character n-grams closer to token unigrams.

LP keeps its peak at 10, but now even lower than for the token n-grams For gender, the system checks the profile for about common male and common female first names, as well as for gender related words, such as father, mother, wife and husband. Possibly, the other n-grams are just mirroring this quality of the unigrams, with the effectiveness of the mirror depending on how well unigrams are represented in the n-grams.

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In this case, it would seem that the systems are Dating profile adjectives off by the political texts. The tokenizer is able to identify hashtags and Twitter user names to the extent that these conform to the conventions used in Twitter, i.

For the bigrams Figure 2we see much the same picture, although there are differences in the details. Identity disclosed with permission. The control shell then weighted each score by multiplying it by the class separation value on the development data for the settings in question, Welke vragen stellen bij online dating derived the final score by averaging.

On re examination, we see a clearly male first name and also profile photo.

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In this section, we will attempt to get closer to the answer to this question. A new version of this license is available. Apart from normal tokens like words, numbers and dates, it is also able to recognize a wide variety of emoticons.

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From this point on in the discussion, we will present female confidence as positive numbers and male as negative. And also some more negative emotions, such as haat hate and pijn pain. Bigrams Two adjacent tokens. The dotted line represents exactly opposite scores for the two genders.