Hokjes dating games,

For each system, we provided the first N principal components for various N. The dotted line represents exactly opposite scores for the two genders.

One gets the impression that gender recognition is more sociological than linguistic, showing what women and men were blogging about back in A later study Goswami et al. And also some more negative emotions, such as haat hate and pijn pain.

For those techniques where hyperparameters need to be selected, we used a leave-one-out strategy on the test material. When using all user tweets, they reached an accuracy of When we look at his tweets, we see a kind of financial blog, which is an exception in the population we have in our corpus.

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.

The exception also leads to more varied classification by the different systems, yielding a wide range of scores. To test that, we would have to experiment with a new feature types, modeling exactly the difference between the normalized and the Gemini dating compatibility form.

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.

Original 1-gram About features. The ones used more by women are plotted in green, those used more by men in red. And actually checking the existence of a proposed URL was computationally infeasible for the amount of text we intended to process.

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. As scaling is not possible when there are columns with constant values, such columns were removed first.

If we look at the rest of the top males Table 2we may see more varied topics, but the wide recognizability stays.

The age component of the system is described in Nguyen et al. Accuracy Percentages for various Feature Types and Techniques. With only token unigrams, the recognition accuracy was We represent this quality by the class separation value that we described in Section 4.

However, his Twitter network contains mostly female friends. Because of the way in which SVR does its classification, hyperplane separation in a transformed version of the vector space, it is impossible to determine which features do the most work.

Even the character 5-grams have ranks up to 40 for this top The age is reconfirmed by the endearingly high presence of mama and papa. For all feature types, we used only those features which were observed with at least 5 authors in our whole collection for skip bigrams 10 authors.

Although LIWC appears a very interesting addition, it hardly adds anything to the classification. However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata.

Gender Recognition on Dutch Tweets - PDF

And by TweetGenie as well. The most extreme misclassification is reserved for a female, author We used the n-grams with n from 1 to 5, again only when the n-gram was observed with at least 5 authors. Here the grid search investigated: Gender recognition has also already been applied to Tweets.

We expect that the performance with TiMBL can be improved greatly with the development of a better hyperparameter selection mechanism. Another interesting group of authors is formed by the misclassified ones. For the other feature types, we see some variation, but most scores are found near the top of the lists.

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