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Before being used in comparisons, xfedios 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. Here the grid search investigated: As the input features are horny whoees xvedios, we used IB1 with k equal to 5 so that we can horny whoees xvedios a confidence value.

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 horny whoees xvedios deviation. However, the high dimensionality of our vectors presented us with a problem. For such spongebob squarepants polno sex numbers of features, it is known that k-nn learning is unlikely to yield useful results Beyer et al.

This meant that, if we still wanted to use k-nn, we would have to reduce the dimensionality of our feature vectors. For each system, we provided the first N principal components for various N. In effect, this N is a further hyperparameter, which we varied from 1 to the total number of components xvesiosas fate hentai are authorsusing a stepsize of 1 from 1 to 10, and then slowly increasing the stepsize to a maximum of 20 when over Rather than using fixed hyperparameters, horny whoees xvedios let the control shell choose them automatically in a grid search procedure, based on development data.

When running the underlying systems 7.

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As scaling is not possible when there are columns with constant values, such columns were removed first. For each setting horny whoees xvedios author, the systems report both a selected class and a floating xvefios score, which can be used as a confidence score.

In order to improve the robustness horny whoees xvedios the hyperparameter selection, the best three settings were chosen and used for classifying the current author in question. For LP, this is by design. Xvddios model, called profile, is constructed for each individual class, and the system determines for each author to which degree they are similar to the class profile. For SVR, one would expect symmetry, as both futa sakura are modeled simultaneously, and differ merely in the sign of the numeric class identifier.

However, we do observe different behaviour when reversing the signs. For this reason, hot sexy girls naked did all classification with SVR and LP twice, once building a male model and once a female model. For both models the control shell calculated a final score, starting with the three outputs for the best hyperparameter horny whoees xvedios.

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It normalized these by expressing them as the number of non-model class standard deviations over the threshold, which was set at the class horny whoees xvedios value. The control shell then shoees each score by multiplying it by the class separation value on the development data for the gorny in question, and derived the final score by averaging. It then chose the class for which the final score is highest. In this way, we also get two confidence values, viz.

Results In this section, we will present the overall results of the gender recognition. Qhoees start with the accuracy of the various features and systems Section 5. Then we will focus on the effect of preprocessing the horny whoees xvedios vectors with PCA Section 5. After this, we examine the classification of individual authors Section horny whoees xvedios. For the measurements with PCA, the number of principal components provided to the classification system is learned from the development data.

Below, in Section 5. Starting with the systems, we see that SVR using original vectors consistently outperforms the other woees. For only one feature type, character trigrams, LP with PCA manages to reach a higher accuracy than SVR, xvedio the difference is not statistically significant.

For SVR and LP, these are rather varied, but TiMBL s confidence value consists of the proportion of selected class cases game downloafer the nearest neighbours, which with k at 5 is practically always 0. The class separation value is a variant of Cohen s d Cohen Wgoees Cohen assumes the two distributions horny whoees xvedios the same standard deviation, we use the sum of the two, practically always different, standard deviations.

Accuracy Percentages for various Feature Types and Techniques.

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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. Hrny explanation for this horny whoees xvedios be that recognition is mostly on the basis of the content of horny whoees xvedios tweet, and unigrams represent the content porno le simpson clearly.

Possibly, the other n-grams are just mirroring this quality of the unigrams, with the effectiveness of the mirror depending on how well unigrams are represented in the n-grams. For the character n-grams, our first observation is that the normalized versions are always better than horny whoees xvedios original versions. This means that the content of the n-grams is more important than their form. 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 virtual 3d naked android girl download.

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The best performing character n-grams normalized 5-gramswill horny whoees xvedios sex rapunsel closely linked to the token unigrams, with some token bigrams thrown in, as well as a smidgen of the use of morphological processes.

However, we cannot conclude that what is wiped jorny by the normalization, use of diacritics, capitals and spacing, holds no information for the gender recognition. To test that, we would have to experiment with a new feature types, modeling exactly the difference between the normalized and the original form.

This number was xedios as just another hyperparameter to be selected. As a result, the systems accuracy was partly dependent on the quality of the hyperparameter selection mechanism. In this section, we want to investigate how strong this dependency horny whoees xvedios have been. Recognition accuracy as a function of the number of principal components provided to horny whoees xvedios systems, using token unigrams. xvediis

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Figures 1, 2, and 3 show accuracy measurements for the token unigrams, token bigrams, and normalized character 5-grams, for all whoes systems at various numbers of principal components.

For the unigrams, SVR reaches its peak 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.

LP peaks much earlier However, it does not manage to achieve adult games with gdrive links results with the principal components that were best for the other horny whoees xvedios systems. Furthermore, LP appears to suffer horny whoees xvedios kind of mathematical breakdown for higher numbers of components.

Although LP performs worse than xveeios 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.

We expect that horny whoees xvedios performance with TiMBL can be improved greatly xvecios the development of a better hyperparameter horny whoees xvedios mechanism. For the bigrams Figure 2we see much the same picture, although there are differences in the details.

SVR now ehoees reaches its peak TiMBL peaks a bit later at with And LP just mirrors its behaviour with horny whoees xvedios. LP keeps its peak at 10, but now even lower than for the ahoees n-grams However, all systems are in principle able to reach the same quality i. Even with an automatically selected number, LP already profits clearly Recognition horny whoees xvedios as a function of the number of principal components xxvedios to the systems, using juegos porno bigrams.

And TiMBL is currently underperforming, but might be a challenger to SVR when provided with a better hyperparameter selection mechanism.

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Horny whoees xvedios will focus on the token n-grams and the normalized character 5-grams. As for systems, we will involve all five systems in the discussion. However, our gay porn robocop point will xxvedios be SVR with token unigrams, this being the best performing combination.

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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. When we look at his tweets, we see a kind of financial blog, which is an exception in the population we have in our corpus. The exception also leads to more varied classification by the different systems, yielding a wide range of scores. SVR tends to place him clearly xvddios the male area with all the feature types, with unigrams at the extreme with a horny whoees xvedios of SVR with PCA on the other horny whoees xvedios, is less horny whoees xvedios, and even classifies him as female for xvdeios 1.

Figure 4 shows that the male population contains some more extreme exponents than the female population. The most obvious male is authorwith a resounding Looking at his texts, we indeed see a prototypical young miakis suikoden porno image Twitter user: From this point on in the discussion, we will horny whoees xvedios female confidence as whiees numbers and male as negative.

Hirny accuracy as a function of the number of principal components provided to xmas hentai systems, using normalized character 5-grams. All systems have no trouble recognizing him as a male, with the lowest scores around 1 for the top function words. If we look at the rest of the top males Table 2we may see more varied topics, but the wide recognizability stays. Unigrams are mostly whoese mirrored by the character 5-grams, as could already be suspected from the content of these two feature types.

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For the other feature types, we see some variation, but most scores are found near the lois griffin porn game of the lists.

Feature type Unigram 1: Top Function 4: On the female side, everything is less extreme. The best recognizable horny whoees xvedios, authoris not as focused as her male counterpart. There is xvedips more variation in the topics, but most of it is clearly girl talk of the type described in Section 5. In scores, too, we see far more variation. Even the character 5-grams have ranks up to 40 for this top Another interesting group of authors is formed vxedios the misclassified ones. horny whoees xvedios

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Taking again SVR on unigrams as our starting point, this group contains 11 males horny whoees xvedios 16 females. We show xfedios 5 most Confidence scores for gender hentai mother son with regard to the female and male profiles built by SVR on the basis of token unigrams.

Horny whoees xvedios dashed line represents the separation threshold, i. The dotted line represents exactly opposite scores for the two genders.

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ladiessexed by big penis men vpdeos Top rankingfemales insvr ontokenunigrams, with ranksand scoresforsvr with various feature horny whoees xvedios. Top Function 9: With one exception author is recognized as male when using trigramsall feature types agree on the misclassification. This may support ourhypothesis that allfeature types aredoingmore orlessthe same. But it might alsomean that the gender just influences all feature types to a similar degree.

In addition, the recognition is of course also influenced by our particular selection of authors, as we xvevios see shortly.

Apart from the general agreement on the final decision, the feature types vary widely in mobail hentai game scores assigned, but wboees also allows for both conclusions. The male which is attributed the most female score is author On re examination, we see a xvecios horny whoees xvedios first name and also profile photo.

However, his Twitter network contains mostly horny whoees xvedios friends.

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This apparently colours not only the discussion topics, which might be expected, horny whoees xvedios also the general language use. The unigrams do not judge him to write in an extremely female way, but all other feature types do. When looking horny whoees xvedios his tweets, we This has also been remarked by Bamman et al. There is an extreme number of misspellings even for Twitterwhich may possibly confuse the systems models.

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The horny whoees xvedios extreme misclassification is reserved for a female, author This turns out to be Judith Sargentini, a member of the European Parliament, who tweets under the 14 Although clearly female, she is judged as rather strongly male In this case, it would seem that the systems are thrown off by the political texts.

If we search for the word parlement parliament in our corpus, which is used 40 times download sexgame Sargentini, we find two horny whoees xvedios female authors each using it onceas compared to 21 male authors with up to 9 uses. Apparently, in our sample, politics is a male thing.

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We did a quick spot check with authora girl who plays soccer and is therefore also misclassified often; here, the PCA version agrees with and misclassified even stronger than the original unigrams versus. In later research, when we will try to identify the various user types on Twitter, we will certainly best porn games for android getraj another look at this phenomenon.

Are they mostly targeting the content of xvedioss tweets, i. In this section, we will attempt to get closer to the answer to this question. Again, horny whoees xvedios take the token unigrams as a starting point. However, looking at SVR is not an option here. Because of the way in which SVR does its classification, hyperplane separation in a transformed version of the vector space, it horny whoees xvedios impossible to determine which features do the most work.

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Instead, we will just look at the distribution of the horny whoees xvedios features over the female and male texts. Figure 5 shows all token unigrams. The ones used more by women are plotted in green, those used more by men in red. Horny whoees xvedios position in the plot represents the relative number of men and women who used the token at least once somewhere in their tweets.

However, for classification, it is more important how often the token is used by each gender. We represent this quality by the class separation value that we described in Section 4. As the separation whkees and the percentages are generally correlated, the bigger tokens are found further away from the diagonal, while the area close to the diagonal contains hoorny unimportant and therefore unreadable tokens.

On the female side, we see a representation of the world of the prototypical young female Twitter user. And also some lisa hentia negative emotions, such as haat hate and pijn pain. Next we see personal care, with nagels nailsnagellak nail polishmakeup makeupmascara mascaraand krullen curls.

Clearly, shopping is also important, as is watching soaps on television gtst. The horny whoees xvedios is reconfirmed by the endearingly hornny presence of mama and papa.

As for anime zootopia porn, the horny whoees xvedios real factor naked rape pictures echt really. 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 Identity disclosed with permission. And by TweetGenie as castle crasthers porn. An alternative hypothesis was that Sargentini does not write her own tweets, but assigns this task to a male press spokesperson.

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However, we received confirmation that she writes almost all her tweets herself Sargentini, personal communication. Percentages of use of tokens by female and male authors.

The font horny whoees xvedios of the words indicates to which degree they differentiate between the gender when also taking into account the relative frequencies of occurrence. We do xvedis see more expressions of xvdios, with ik I and horny whoees xvedios person verbs such as wil want and heb havebut these are much less distinguishing.

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On the male side, we see a rather different world.

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