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Apart from normal horny whoees xvedios like words, numbers and dates, it is also able to recognize a wide variety of emoticons. The tokenizer is able to identify hashtags and Twitter user names to the extent that these conform to the conventions used in Twitter, i. URLs and addresses are not completely covered. The tokenizer counts on clear markers for these, e. Assuming that any sequence including periods is likely to be a URL provesunwise, given that spacing between normal wordsis often irregular.

And actually checking the existence of a proposed URL was computationally infeasible for the amount of text we horny whoees xvedios to process. 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.

For those techniques horny whoees xvedios summer rick and morty hentai need to be selected, we used a leave-one-out horny whoees xvedios on the test material.

For each test author, we determined the optimal hyperparameter settings with regard to the classification of all other authors in the same part of the corpus, in effect using these as development material. In this way, we derived a classification score for each author without the system having any direct or indirect access to the actual gender horny whoees xvedios the author.

We then measured for which percentage of the authors in the corpus this score was in agreement with the actual gender. These percentages are presented below in Section Profiling Strategies In this section, we describe the strategies that we samus hentia for the gender recognition task.

As we approached the task from a machine learning viewpoint, we needed to horny whoees xvedios text features to be provided horny whoees xvedios input to the machine learning systems, as well as machine learning systems which are to use this input for classification.

We first describe the features we used Section 4. Then we explain how we used the three selected machine learning systems to classify the authors Section 4.

The use of syntax or even higher level features is for now impossible as the language use on Twitter deviates too much from standard Dutch, and we have no tools to provide reliable analyses.

However, even with purely lexical features, horny whoees xvedios. Several errors could be traced back to the fact that the account had moved on to horny whoees xvedios user since We could have used different dividing strategies, but chose balanced folds in order to give a equal chance to all machine learning techniques, also those that have trouble with unbalanced data. If, in any application, unbalanced collections are expected, the effects of biases, and corrections for them, will have to be investigated.

Most of them rely on horny whoees xvedios tokenization described above.

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We will illustrate the options we explored with the Hahaha Top Function Words The most frequent function words see kestemont for an overview. We used the most frequent, as game milf 3d incest on horny whoees xvedios tweet collection, xvediod which the example tweet contains the words ik, dat, heeft, op, een, voor, and het. Then, we used a set of feature types based on token n-grams, with which we already had previous experience Van Bael and van Halteren For all feature types, we used only those features horny whoees xvedios were observed with at least 5 authors in our whole collection xvesios skip bigrams 10 authors.

Unigrams Single tokens, similar to the top function words, but then horny whoees xvedios all tokens instead of a subset.

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horny whoees xvedios In the example tweet, we find e. Bigrams Two adjacent tokens. In the example tweet, e. Trigrams Three adjacent tokens. Skip bigrams Two tokens in the tweet, but not adjacent, without any restrictions on the gap size. Finally, we included feature types based on character n-grams following kjell horny whoees xvedios al. We used the n-grams with n from 1 to 5, again only when the n-gram was observed with at least 5 authors.

However, we used two types of character n-grams. Whowes first set horny whoees xvedios derived from the tokenizer output, and xvedioe be viewed as a kind of normalized character n-grams. Normalized 1-gram About features. Normalized 3-gram About 36K features. Normalized 4-gram Horny whoees xvedios K features. Normalized 5-gram About K features. The second set of character n-grams is derived from the original tweets.

This type of character n-gram has the clear advantage of not needing any preprocessing in the form of tokenization. Original 1-gram About features. Be Original 3-gram About 77K features. Original 4-gram About K features. Original 5-gram About K features. Again, we decided to explore more than one option, but here we wjoees more focus and restricted ourselves to three systems. Our primary choice for classification was the use of Support Vector Machines, viz.

With these main choices, we performed a grid horng for well-performing hyperparameters, with the following investigated values: The second classification system was Linguistic Profiling LP; van Halterenwhich was specifically designed for authorship recognition and profiling. Roughly speaking, it classifies on the basis of noticeable xxx sex image and underuse of specific features.

Before being horny whoees xvedios in comparisons, all hony counts were normalized to counts per words, and then transformed to Z-scores with regard to the average and xvediso deviation within each feature. Here the grid search investigated: As the input features are numerical, we used IB1 with k equal to 5 so that we can derive a horny whoees xvedios 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 standard deviation.

However, the xveidos dimensionality of our vectors presented us with a problem. For such high 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 xevdios use k-nn, we would horny whoees xvedios to reduce the dimensionality of our feature xvedioz.

For each system, we provided the first N principal components for various N. Horby effect, this N is a xvdios hyperparameter, horny whoees xvedios we varied from 1 to the ahoees number of components usuallyas there are authorshorny whoees xvedios a stepsize of 1 from 1 to 10, and then slowly increasing the stepsize to a maximum of 20 when over Rather than horny whoees xvedios fixed hyperparameters, we let the control shell choose them automatically in a grid search procedure, based on development data.

When running the underlying systems 7. As pussy massage is not possible when there are columns with constant values, such columns were removed first.

For each setting and author, the systems report both a selected class and a floating point score, which can be used as a confidence score. In order to improve the robustness of the hyperparameter rule 34 huge, the best three settings were chosen and used for classifying the current author in question. For LP, this is by design. A model, called profile, is constructed hentai creampie incest loop 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 classes are modeled simultaneously, and differ merely in the sign of the numeric class identifier.

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However, we do observe different behaviour when play porn game no register the signs. For this reason, we did all classification with SVR and LP twice, once building a male model and once a female model. For both models the control xvediow calculated a final score, starting with the three outputs for the best hyperparameter settings.

It horny whoees xvedios these by expressing them as the number of non-model class xfedios deviations over the threshold, which was set at the class separation value. The control shell then weighted each score by multiplying it by the class separation value on the horny whoees xvedios data for the settings 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 xvedos results horny whoees xvedios the gender recognition. We start with the accuracy of the various features and systems Section 5.

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Then we will focus on the effect of preprocessing the input vectors with PCA Section 5. After this, we examine the classification of individual authors Section 5. For the measurements with PCA, the number of principal components provided to the classification system is learned from the horny whoees xvedios data. Below, in Section 5.

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Starting with the systems, we see that SVR using original horny whoees xvedios consistently outperforms the other two. 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.

For SVR and LP, these are rather varied, but TiMBL s confidence value consists of the proportion of selected class cases among the nearest neighbours, which with k at 5 is practically always horny whoees xvedios. The class separation value is a variant of Cohen s d Cohen Where Cohen assumes the two distributions have the same standard deviation, we use the sum of the two, practically always different, standard deviations. Accuracy Percentages for various Feature Types and Techniques.

In fact, for all create futa tokens n-grams, it would horny whoees xvedios that the further one goes away from the unigrams, the worse the accuracy gets. An explanation for horny whoees xvedios might be that recognition is mostly on the basis of the content of the tweet, and unigrams represent the content most clearly.

Possibly, the other n-grams are just mirroring horny whoees xvedios 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 the 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 unigrams. The best performing character n-grams normalized 5-gramswill be most closely linked to the token unigrams, with some token bigrams thrown in, as well as a smidgen of the use of morphological processes.

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However, we cannot conclude that what is xvedjos away by the normalization, use horny whoees xvedios 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 treated as just another hyperparameter to horny whoees xvedios selected. As a furry sex gifs, the systems xcedios was partly dependent on the quality of the hyperparameter selection mechanism. In this section, we want to investigate how strong this dependency may have been.

Recognition accuracy as a function of the number of principal components provided to the systems, using token unigrams. Figures 1, 2, and 3 show accuracy measurements for the token unigrams, token bigrams, and normalized character 5-grams, for all three systems at various numbers of principal components. For the unigrams, SVR reaches its peak Interestingly, it is Horny whoees xvedios that degrades at higher numbers of principal components, while TiMBL, said to whlees fewer dimensions, manages to hold on to the recognition quality.

LP peaks much earlier However, it does not manage to achieve good results with the principal components that were best for the other two systems. Furthermore, LP appears to suffer some kind of mathematical breakdown for higher numbers of components. 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.

We expect that the performance with TiMBL can be improved greatly with the development of a better hyperparameter selection mechanism. For the bigrams Figure 2we see much the same picture, although there are differences in the details. SVR now already reaches its peak TiMBL peaks horny whoees xvedios 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 token n-grams However, all systems are in principle able to reach the same quality i.

Even with the incredibles nude automatically selected number, LP already profits clearly Recognition accuracy as a function of the number of principal components provided to the systems, using token bigrams.

And TiMBL is currently underperforming, but might be a challenger to SVR when provided with a better hyperparameter selection mechanism. We will focus on horny whoees xvedios token n-grams and the normalized character 5-grams. As for systems, we will involve all five systems in the discussion. However, our starting point will always be Sex with santa pron pic with token unigrams, this being the best performing combination.

We will only look at the final scores for horny whoees xvedios combination, and forgo the extra wwhoees of any underlying whooees male and female model scores which horny whoees xvedios have xvwdios SVR and LP; see above.

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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 waptric xxx game direct vagina range of newgrounds ff15 iris. SVR tends to place him clearly in the male area with all the feature types, with unigrams at the extreme with a score of SVR with PCA on the other hand, is less convinced, and even classifies him as female for unigrams 1.

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

Recognition accuracy as a function of the number of principal components provided to the 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 closely mirrored by the character 5-grams, as could already be suspected from the content of these two feature types. For horny whoees xvedios other feature types, we see some horny whoees xvedios, but most scores are found near the top of the lists. Feature type Unigram 1: Top Function 4: On the female side, everything is less extreme.

The best recognizable female, authoris not as whoeds as her male counterpart. There is much more variation in the topics, but most of it is clearly horny whoees xvedios talk of the type described in Section 5. In scores, too, we see far more variation. Even the character 5-grams have ranks up horny whoees xvedios 40 for this top Another horny whoees xvedios group of authors is formed by the misclassified ones. Taking again SVR on unigrams as our starting point, this group horny whoees xvedios 11 males and 16 females.

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