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The first set is derived from the tokenizer output, and can be viewed as a kind of normalized character n-grams. From this point on in the discussion, we will present female confidence as positive numbers and male as negative.
However, we used two types of character n-grams. Finally, we included feature types based on character n-grams following kjell et al. After this, we examine the classification of individual authors Section 5.
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Normalized 4-gram About K features. From each user s tweets, we removed all retweets, as these did not contain original text by the author. Next we see personal care, with nagels nailsnagellak nail polishmakeup makeupmascara mascaraand krullen curls.
URLs and addresses are not completely covered. Unigrams are mostly closely mirrored by the character 5-grams, as could already be suspected from the content of these two feature types.
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Bigrams Two adjacent tokens. No warranties are given. 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. Figure 4 shows that the male population contains some more extreme exponents than the female population.
Then we describe our experimental data and the evaluation method Section 3after which we proceed to describe the various author profiling strategies that we investigated Section 4. In effect, this N is a further hyperparameter, which we varied from 1 to the total number of components usuallyas there are authorsusing a stepsize of 1 from 1 to 10, and Legal age dating slowly increasing the stepsize to a maximum of 20 when over 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.
For the measurements with PCA, the number of principal components provided to the classification system is learned from the development data. Recognition accuracy as a function of the number of principal components provided to the systems, using normalized character 5-grams.
An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network.
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