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

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Groups the evaluated policies from all providers that can be configured.

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

In this section, we will attempt to get closer to the answer to this question. Trigrams Three adjacent tokens. From each user s tweets, we removed all retweets, as these did not contain original text by the author.

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Another system that predicts the gender for Dutch Twitter users is TweetGenie http: 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. The tokenizer counts on clear markers for these, e.

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Normalized 3-gram About 36K features. With these main choices, we performed a grid search for well-performing hyperparameters, with the following investigated values: Some policies can only be configured at the device level, meaning the policy will take effect independent of who is logged into the device.

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Although LP performs worse than it could on fixed numbers of principal components, its more detailed confidence score allows a better hyperparameter selection, on average selecting around 9 principal components, where TiMBL chooses a wide range of numbers, and generally far lower than is optimal.

The male which is attributed the most female score is author This may support ourhypothesis that allfeature types aredoingmore orlessthe same.

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Unigrams Single tokens, similar to the top function words, but then using all tokens instead of a subset. Clearly, shopping is also important, as is watching soaps on television gtst.

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Finally, we included feature types based on character n-grams following kjell et al. With lexical N-grams, they reached an accuracy of And by TweetGenie as well.

However, as research shows a higher number of female users in all as well Heil and Piskorskiwe do not view this as a problem.

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