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

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The position in the plot represents the relative number of men and women who used the token at least once somewhere in their tweets.

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The only hyperparameters we varied in the grid search are the metric Numerical and Cosine distance and the weighting no weighting, information gain, gain ratio, chi-square, shared variance, and standard deviation.

In the example tweet, e. On the female side, we see a representation of the world of the prototypical young female Twitter user. Results In this section, we will present the overall results of the gender recognition. One gets the impression that gender recognition is more sociological than linguistic, showing what women and men were blogging about back in A later study Goswami et al.

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The male which is attributed the most female score is author However, our starting point will always be SVR with token unigrams, this being the best performing combination.

The word haar may be the pronoun her, but just as well the noun hair, and in both cases it is actually more related to the Apart from normal tokens like words, numbers and dates, it is also able to recognize a wide variety of emoticons. Finally, as the use of capitalization and diacritics is quite haphazard in the tweets, the tokenizer strips all words of diacritics and transforms them to lower case.

Experimental Data and Evaluation In this section, we first describe the corpus that we used in our experiments Section 3.

Be Original 3-gram About 77K features. We used the n-grams with n from 1 to 5, again only when the n-gram was observed with at least 5 authors.

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And, obviously, it is unknown to which degree the information that is present is true. Confidence scores for gender assignment with regard to the female and male profiles built by SVR on the basis of token unigrams. In Koppel et al. For the character n-grams, our first observation is that the normalized versions are always better than the original versions.

LP keeps its peak at 10, but now even lower than for the token n-grams Raw veganism includes only fresh and uncooked fruit, nuts, seeds, and vegetables. This type of character n-gram has the clear advantage of not needing any preprocessing in the form of tokenization.

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.

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SVR now already reaches its peak With lexical N-grams, they reached an accuracy of 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.

All users, obviously, should be individuals, and for each the gender should be clear.

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Gender recognition has also already been applied to Tweets. 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.

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Pescetarianismwhich includes fish and possibly other forms of seafood. For only one feature type, character trigrams, LP with PCA manages to reach a higher accuracy than SVR, but the difference is not statistically significant. Olives and olive oil are another important plant source of unsaturated fatty acids.

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Normalized 4-gram About K features. There is an extreme number of misspellings even for Twitterwhich may possibly confuse the systems models. Here the grid search investigated: Veganism excludes all animal flesh and by-products, such as milk, honey not always[34] and eggs, as well as items refined or manufactured through any such product, such as animal-tested baking soda or white sugar refined with bone char.

In this way, we also get two confidence values, viz. For SVR, one would expect symmetry, as both classes are modeled simultaneously, and differ merely in the sign of the numeric class identifier.

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We achieved the best results,