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

Ervaringen dating hoger opgeleiden. The daily wtf: curious perversions in information technology

Below, in Section 5. In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section. We will only look at the final scores for each combination, and forgo the extra detail of any underlying separate male and female model scores which we have for SVR and LP; see above.

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 start with the accuracy of the various features and systems Section 5.

And by TweetGenie as well. The ones used more by women are plotted in green, those used more by men in red. However, the high dimensionality of our vectors presented us with a problem. Juola and Koppel et al. The license may not give you all of the permissions necessary for your intended use.

Then we will focus on the effect of preprocessing the input vectors with PCA Section 5.

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However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata. However, as research shows a higher number of female users in all as well Heil and Piskorskiwe do not view this as a problem.

In this case, it would seem that the systems are thrown off by the political texts.

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There is an extreme number of misspellings even for Twitterwhich may possibly confuse the systems models. With one exception author is recognized as male when using trigramsall feature types agree on the misclassification. We will focus on the token n-grams and the normalized character 5-grams.

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We checked gender manually for all selected users, mostly on the basis 3. Gender recognition has also already been Benefits of dating a christian man to Tweets.

As a result, the systems accuracy was partly dependent on the quality of the hyperparameter selection mechanism.

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This apparently colours not only the discussion topics, which might be expected, but also the general language use. In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields.

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The exception also leads to more varied classification by the different systems, yielding a wide range of scores. The best performing character n-grams normalized 5-gramswill be most closely linked to the token unigrams, with some token bigrams thrown in, as well as a smidgen of the use of morphological processes.

In the example tweet, e. As the input features are numerical, we used IB1 with k equal to 5 so that we can derive a confidence value.

As we approached the task from a machine learning viewpoint, we needed to select Ervaringen dating hoger opgeleiden features to be provided as input to the machine learning systems, as well as machine learning systems which are to use this input for classification.

In scores, too, we see far more variation.

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Gender Recognition Gender recognition is a subtask in the general field of authorship recognition and profiling, which has reached maturity in the last decades for an overview, see e. 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.

For each system, we provided the first N principal components for various N. URLs and addresses are not completely covered. Accuracy Percentages for various Feature Types and Techniques.

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