Dating voor 60 plussers. Gender recognition on dutch tweets - pdf

The conclusion is not so much, however, that humans are also not perfect at guessing age on the basis of language use, but rather that there is a distinction between the biological and the social identity of authors, and language use is more likely to represent the social one cf.

This has also been remarked by Bamman et al.

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Then we explain how we used the three selected machine learning systems to classify the authors Section 4. We will only look at the Online dating successful marriages 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.

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For SVR, one would expect symmetry, as both classes are modeled simultaneously, and differ merely in the sign of the numeric class identifier. The most extreme misclassification is reserved for a female, author Recognition accuracy as a function of the number of principal components provided to the systems, using token unigrams.

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. The age component of the system is described in Nguyen et al.

On the Dating voor 60 plussers side, we see a representation of the world of the prototypical young female Twitter user.

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. In scores, too, we see far more variation.

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Identity disclosed with permission. Their highest score when using just text features was 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.

The authors do not report the set of slang words, but the non-dictionary words appear to be more related to style than to content, showing that purely linguistic behaviour can contribute information for gender recognition as well.

We represent this quality by the class separation value that we described in Section 4. And LP just mirrors its behaviour with unigrams.

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We then measured for which percentage of the authors in the corpus this score was in agreement with the actual gender. Apart from normal tokens like words, numbers and dates, it is also able to recognize a wide variety of emoticons.

Even the character 5-grams have ranks up to 40 for this top For the unigrams, SVR reaches its peak Although we agree with Nguyen et al.

The best recognizable female, authoris not as focused as her male counterpart. 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.

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In this way, we also get two confidence values, viz. And, obviously, it is unknown to which degree the information that is present is true.

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This restriction brought the number of users down to aboutHowever, we do observe different behaviour when reversing the signs. Apart from the general agreement on the final decision, the feature types vary widely in the scores assigned, but this also allows for both conclusions.

It then chose the class for which the final score is highest.

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From this material, we considered all tweets with a date stamp in and In all, there were about 23 million users present. The first set is derived from the tokenizer output, and can be viewed as a kind of normalized character n-grams. Roughly speaking, it classifies on the basis of noticeable over- and underuse of specific features.