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

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SVR now already reaches its peak This restriction brought the number of users down to aboutThey used lexical features, and present a very good breakdown of various word types. Juola and Koppel et al.

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If no cue is found in a user s profile, no gender is assigned. In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section.

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. Ang dating daan milan italy test that, we would have to experiment with a new feature types, modeling exactly the difference between the normalized and the original form.

As in our Dating cupid free experiment, this measurement is based on Twitter accounts where the user is known to be a human individual.

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Again, we take the token unigrams as a starting point. 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.

With one exception author is recognized as male when using trigramsall feature types agree on the misclassification. In this section, we will attempt to get closer to the answer to this question. Interestingly, it is SVR that degrades at higher numbers of principal components, while TiMBL, said to need fewer dimensions, manages to hold on to the recognition quality.

This means that the content of the n-grams is more important than their form.

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.

Recognition accuracy as a function of the number of principal components provided to the systems, using token bigrams. However, looking at SVR is not an option here. And also some more negative emotions, such as haat hate and pijn pain.

For each test author, we determined the optimal hyperparameter settings with regard to the classification of all other authors in the same part of the corpus, in effect using these as development material.

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Below, in Section 5. Assuming that any sequence including periods is likely to be a URL provesunwise, given that spacing between normal wordsis often irregular. 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.

The age is reconfirmed by the endearingly high presence of mama and papa. The male which is attributed the most female score is author Normalized 1-gram About features.

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And by TweetGenie as well. On re examination, we see a clearly male first name and also profile photo. For LP, this is by design.

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.

Slightly more information seems to be coming from content Finally, we included feature types based on character n-grams following kjell et al.

Recognition accuracy as a function of the number of principal components provided to the systems, using token unigrams. However, our starting point will always be SVR with token unigrams, this being the best performing combination.

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As a result, the systems accuracy was partly dependent on the quality of the hyperparameter selection mechanism. Accuracy Percentages for various Feature Types and Techniques.

For the unigrams, SVR reaches its peak Be Original 3-gram About 77K features.

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