Gender Recognition on Dutch Tweets - PDF Gender Recognition on Dutch Tweets - PDF

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In scores, too, we see far more variation. An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network.

In this paper, we start modestly, by attempting to derive just the gender of the authors 1 automatically, purely on the basis of the content of their tweets, using author profiling techniques. But it might alsomean that the gender just influences all feature types to a similar degree.

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.

Top Function Words The most frequent function words see kestemont for an overview. The dashed line represents the separation threshold, i. This may support ourhypothesis that allfeature types aredoingmore orlessthe same.

For the bigrams Figure 2we see much the same picture, although there are differences in the details. With these main choices, we performed a grid search for well-performing hyperparameters, with the following investigated values: Normalized 5-gram About K features.

Normalized 1-gram About features. Then, as several of our features were based on tokens, we tokenized all text samples, using our own specialized tokenizer for tweets.

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Results In this section, we will present the overall results of the gender recognition. The most obvious male is authorwith a resounding Looking at his texts, we indeed see a prototypical young male Twitter user: Roughly speaking, it classifies on the basis of noticeable over- and underuse of specific features.

Experimental Data and Evaluation In this section, we first describe the corpus that we used in our experiments Section 3. These percentages are presented below in Section Profiling Strategies In this section, we describe the strategies that we investigated for the gender recognition task.

In effect, this N is a further hyperparameter, which we varied from 1 to the total number of components usuallyas there are authorsusing a stepsize of 1 from 1 to 10, and then slowly increasing the stepsize to a maximum of 20 when over Figure 4 shows that the male population contains some more extreme exponents than the female population.

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This type of character n-gram has the clear advantage of not needing any preprocessing in the form of tokenization. However, all systems are in principle able to reach the same quality i.

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Slightly more information seems to be coming from content The class separation value is a variant of Cohen s d Cohen For such high numbers of features, it is known that k-nn learning is unlikely to yield useful results Beyer et al. For only one feature type, character trigrams, LP with PCA manages to reach a higher accuracy than SVR, but the difference is not statistically significant.

Then we describe our experimental data and the evaluation method Section 3after which we proceed to describe the various author profiling strategies that we investigated Section 4.