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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. In the following sections, we first present some previous work on gender recognition Section 2. The second classification system was Linguistic Profiling LP; van Halterenwhich was specifically designed for authorship recognition and profiling.

The class separation value is a variant of Cohen s d Cohen Confidence scores for gender assignment with regard to the female and male profiles built by SVR on the basis of token unigrams.

Recognition accuracy as a function of the number of principal components provided to the systems, using normalized character 5-grams.

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Be Original 3-gram About 77K features. Again, we decided to explore more than one option, but here we preferred more focus and restricted ourselves to three systems.

All systems have no trouble recognizing him as a male, with the lowest scores around 1 for the top function words. The position in the plot represents the relative number of men and women who used the token at least once somewhere in their tweets.

We also varied the recognition features provided to the techniques, using both character and token n-grams. With only token unigrams, the recognition accuracy was The best recognizable female, authoris not as focused as her male counterpart. The resource would become even more useful if we could deduce complete and correct metadata from the various available Gratis dating voor jeugd sources, such as the provided metadata, user relations, profile photos, and the text of the tweets.

When Dating hvem betaler more information sources, such as profile fields, they reach an accuracy of Before being used in comparisons, all feature counts were normalized to counts per words, and then transformed to Z-scores with regard to the average and standard deviation within each feature.

Original 1-gram About features. The exception also leads to more varied classification by the different systems, yielding a wide range of scores. Figure 5 shows all token unigrams.

Where Cohen assumes the two distributions have the same standard deviation, we use the sum of the two, practically always different, standard deviations. As for style, the only real factor is echt really. Feature type Unigram Bigram Trigram Skipgram Char 5-gram Top Function 14 get the impression that Dutch is not his native language, which is supported by his name.

On the female side, everything is less extreme. We selected of these so that they get a gender assignment in TwiQS, for comparison, but we also wanted to include unmarked users in case these would be different in nature. Are they mostly targeting the content of the tweets, i. Normalized 5-gram About K features.

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Top Function 9: These percentages are presented below in Section Profiling Strategies In this section, we describe the strategies that we investigated for the gender recognition task. And also some more negative emotions, such as haat hate and pijn pain. An alternative hypothesis was that Sargentini does not write her own tweets, but assigns this task to a male press spokesperson.

We start with the accuracy of the various features and systems Section 5. It normalized these by expressing them as the number of non-model class standard deviations over the threshold, which was set at the class separation value.

However, we used two types of character n-grams. Several errors could be traced back to the fact that the account had moved on to another user since We could have used different dividing strategies, but chose balanced folds in order to give a equal chance to all machine learning techniques, also those that have trouble with unbalanced data.

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The control shell then weighted each score by multiplying it by the class separation value on the development data for the settings in question, and derived the final score by averaging.

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

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If, in any Gratis dating voor jeugd, unbalanced collections are expected, the effects of biases, and corrections for them, will have to be investigated. In fact, for all the tokens n-grams, it would seem that the further one goes away from the unigrams, the worse the accuracy gets.

An explanation for this might be that recognition is mostly on the basis of the content of the tweet, and unigrams represent the content most clearly. For all techniques and features, we ran the same 5-fold cross-validation experiments in order to determine how well they could be used to distinguish between male and female authors of tweets.

We checked gender manually for all selected users, mostly on the basis 3.

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