Erotische massage lelystad masturberende kutjes

erotische massage lelystad masturberende kutjes

Samen met CDA en D66 importeren wij jihadisten - satanisten en islamitische neuk pooiers! Samen met CDA en D66 importeren wij satanisten - jihadisten en islamitische neuk pooiers.

Hogere lasten - meer islam - minder veiligheid - minder  pensioen en bijstand - AOW leeftijd uiteindelijk naar Ze eten schimmelkaas, drinken oude wijn en rijden een auto zonder dak.

En deel de link! Niet dat we er naar luisterden, maar toch. Nauwelijks verholen racisme en seksisme. Niet in Mokum, niet elders. Dat vinden wij niet raar, dat vinden wij bijzonder! Of zou Rob dat doen? Onze trouwe kiezers zijn de voeding die kanker zo hard nodig heeft! Stem Djuri Stoter op 21 maart.

Dat allemaal dankzij ons! Wij kiezen voor doen! U ook op 21 maart? Stem liberaal , brutaal van achter anaal! Ongelovig volk zonder cultuur en identiteit!

Onze winst is binnen want wij hebben  geld en drugshonden! Doe normaal zoals een liberaal! Gratis zorg en onderdak alleen voor onze asielzoekers! Ook in moeilijke tijden kunnen graaien bij zieken - werklozen en bejaarden! In moeilijke tijden kunnen graaien bij zieken - armen  bejaarden! Wij verkozen vuiligheid boven eerlijkheid! Halbe waarheden zul je bedoelen! Onze asielzoekers verdacht want wij importeren en uw dochters mogen creperen! Rot dan maar op. Dat vinden wij beter voor onze toekomst.

Nederland tot shithole omtoveren! Dan willen wij heel graag met jou kennismaken! U in een doos op straat en  uitkering van Amalia euro per dag blijft overeind! Tot zeker het jaar blijven wij de grootste! De bijstand - wajong - AOW en pensioen zijn straks verleden tijd! Samen met CDA en D66 gaan wij verder met de afbraak! Sommige mensen zijn meer gelijk dan anderen. Ook die van de ffd van Rute!!! En nu allemaal 4 jaar je bek houden! Dan heb je pech want bij VVD is alles schrappen heel normaal.

Dan zoek je het zelf maar allemaal lekker uit. Of je nou bankier, CEO of dame van 81 bent. Daar kan ik me weinig meer van herinneren. Dus doe maar een kwartje. En een beetje ket. Meer geld voor de elite. Er zijn belangrijkere dingen aan de hand in Nederland: Schouder aan schouder de hardwerkende arbeider bevrijden. We gaan met de hamer der verzetting de glazen inslaan van de spiegel der onderdrukking. Bier, Moppen, en telefoons. En natuurlijk volhouden 'dat alles netjes geregeld is'.

Ja hoor vraag maar aan marktlakei Kamp. Die heeft vooral schijt aan de burger. Geen flauwe grapjes meer! Nou, hahahahaha, ik weet het niet, hahahaha. Weet jij niet waarom, dan weet zij het wel. Humorloos, Lange tenen, Normen en Waarden arm. Zorg zonder eigen risico. Pensioen heb ik zelf gespaard! 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 et 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. The first set is derived from the tokenizer output, and can be viewed as a kind of normalized character n-grams. Normalized 1-gram About features. Normalized 3-gram About 36K features.

Normalized 4-gram About 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 preferred more focus and restricted ourselves to three systems. Our primary choice for classification was the use of Support Vector Machines, viz. We chose Support Vector Regression ν-svr to be exact with an RBF kernel, as it had shown the best results in several research projects e.

With these main choices, we performed a grid search for well-performing hyperparameters, with the following investigated values: The second classification system was Linguistic Profiling LP; van Halteren , which was specifically designed for authorship recognition and profiling.

Roughly speaking, it classifies on the basis of noticeable over- and underuse of specific features. Before being used in comparisons, all 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 numerical, we used IB1 with k equal to 5 so that we can derive 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 standard deviation. However, the high 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 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 usually , as there are authors , using 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, we let the control shell choose them automatically in a grid search procedure, based on development data.

When running the underlying systems 7. As scaling 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 selection, 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 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 classes 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, we 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 settings.

It normalized these by expressing them as the number of non-model class standard 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 development 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 overall results of the gender recognition. We start with the accuracy of the various features and systems Section 5. 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 development data.

Below, in Section 5. Starting with the systems, we see that SVR using original vectors 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 0.

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 the tokens n-grams, it would seem that the further one goes away from the unigrams, the worse the accuracy gets. An explanation for this 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 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 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-grams , will 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. However, we cannot conclude that what is wiped away 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 treated 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 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 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 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 2 , we see much the same picture, although there are differences in the details.

SVR now already reaches its peak TiMBL peaks a bit later at with And LP just mirrors its behaviour with unigrams. 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 an 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 the 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 SVR with token unigrams, this being the best performing combination. 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 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 male is author , with a resounding Looking at his texts, we indeed see a prototypical young male Twitter 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 2 , we 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 the other feature types, we see some variation, 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, author , is not as focused as her male counterpart. There is much 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 by the misclassified ones. Taking again SVR on unigrams as our starting point, this group contains 11 males and 16 females.

We show the 5 most Confidence scores for gender assignment with regard to the female and male profiles built by SVR on the basis of token unigrams. The dashed line represents the separation threshold, i. The dotted line represents exactly opposite scores for the two genders. Top rankingfemales insvr ontokenunigrams, with ranksand scoresforsvr with various feature types.

Top Function 9: With one exception author is recognized as male when using trigrams , all 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 will see shortly. Apart from the general agreement on the final decision, the feature types vary widely in the scores assigned, but this also allows for both conclusions. The male which is attributed the most female score is author On re examination, we see a clearly male first name and also profile photo.

However, his Twitter network contains mostly female friends. This apparently colours not only the discussion topics, which might be expected, but 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 at his tweets, we This has also been remarked by Bamman et al.

There is an extreme number of misspellings even for Twitter , which may possibly confuse the systems models. The most 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 by Sargentini, we find two more female authors each using it once , as compared to 21 male authors with up to 9 uses. Apparently, in our sample, politics is a male thing. We did a quick spot check with author , a 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 have another look at this phenomenon.

Are they mostly targeting the content of the tweets, i. In this section, we will attempt to get closer to the answer to this question. Again, we 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 is impossible to determine which features do the most work.

Instead, we will just look at the distribution of the various 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. The 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 value and the percentages are generally correlated, the bigger tokens are found further away from the diagonal, while the area close to the diagonal contains mostly 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 more negative emotions, such as haat hate and pijn pain. Next we see personal care, with nagels nails , nagellak nail polish , makeup makeup , mascara mascara , and krullen curls.

Clearly, shopping is also important, as is watching soaps on television gtst. The age is reconfirmed by the endearingly high presence of mama and papa.

As for style, the only real factor is 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 well. An alternative hypothesis was that Sargentini does not write her own tweets, but assigns this task to a male press spokesperson. 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 size of the words indicates to which degree they differentiate between the gender when also taking into account the relative frequencies of occurrence. Spelling Bestuderen Inleiding Op B1 niveau gaan we wat meer aandacht schenken aan spelling.

Je mag niet meer zoveel fouten maken als op A1 en A2 niveau. We bespreken een aantal belangrijke. Understanding and being understood begins with speaking Dutch Begrijpen en begrepen worden begint met het spreken van de Nederlandse taal The Dutch language links us all Wat leest u in deze folder? Als je een onderdeel. Vergaderen in het Engels In dit artikel beschrijven we verschillende situaties die zich kunnen voordoen tijdens een business meeting.

Na het doorlopen van deze zinnen zal je genoeg kennis hebben om je. Online Resource 1 Title: Implementing the flipped classroom: An exploration of study behaviour and student performance Journal: Dus ik durfde het niet aan om op de fiets naar. Document properties Most word processors show some properties of the text in a document, such as the number of words or the number of letters in that document.

Write a program that can determine some of. Waar gaat deze test over? Flash info 1 In the morning I always make my bed. C Sometimes, when I feel like it. List of variables with corresponding questionnaire items in English used in chapter 2 Task clarity 1.

I understand exactly what the task is 2. I understand exactly what is required of. See slides 2 4 of lecture 8. See slides 4 6 of lecture 8. Wouldn t it be great to create your own funny character that will give. Please use the latest firmware for the router. The firmware is available on http: Quick scan method to evaluate your applied educational game light validation 1.

What is an API user? How is it different from other users? What is an operation code? And should I choose "Authorisation" or "Sale"? Assessing writing through objectively scored tests: Aim of this presentation Give inside information about our commercial comparison website and our role in the Dutch and Spanish energy market Energieleveranciers.

My family Main language Dit is de basiswoordenschat. Deze woorden moeten de leerlingen zowel passief als actief kennen. Als je een nieuwe taal wilt spreken en schrijven, heb je vooral veel nieuwe woorden nodig.

Voorstel rondje Wat hoop je te leren? Heb je iets te delen? Wat zegt de Programma Gids? And especially truths that at first sight are concrete, tangible and proven. Firewall van de Speedtouch wl volledig uitschakelen? De firewall van de Speedtouch wl kan niet volledig uitgeschakeld worden via de Web interface: De firewall blijft namelijk op stateful staan.

Every day we see them on TV during the commercial break: Iris marrink Klas 3A. Ik kreeg als opdracht om een dagverslag te maken over Polen. Om een realistisch beeld te krijgen van uw niveau,vragen we u niet langer dan één uur te besteden aan de toets. De toets bestaat uit twee. Circle the things that you can find in the tree house in the text. Classification of triangles A triangle is a geometrical shape that is formed when 3 non-collinear points are joined.

The joining line segments are the sides of the triangle. The angles in between the sides. Invloed van het aantal kinderen op de seksdrive en relatievoorkeur M.

Eshuis Oktober Faculteit Psychologie en Onderwijswetenschappen. Woordenlijst bij hoofdstuk 4 de aanbieding reclame, korting De appels zijn in de a Ze zijn vandaag extra goedkoop. Hij woont helemaal a, zonder familie.

Voeg aan het antwoord van een opgave altijd het bewijs, de berekening of de argumentatie toe. Dutch survival kit This Dutch survival kit contains phrases that can be helpful when living and working in the Netherlands. There is an overview of useful sentences and phrases in Dutch with an English. What is your favourite act? Do you like the dancing performances or would you rather listen.

Wat kan je er mee? Hoe werkt het Gibbs sampling? Na de pauze Achterliggende concepten à Dirichlet distribu5e. Veertien leesteksten Leesvaardigheid A1 Te gebruiken bij: Appel, Aerdenhout Verkoopprijs: Zelfwaardering en Angst bij Kinderen: Self-Esteem and Fear or Anxiety. Duurzaam projectmanagement - De nieuwe realiteit van de projectmanager Dutch Edition Ron Schipper Click here if your download doesn"t start automatically Duurzaam projectmanagement - De nieuwe realiteit.

But the Real Issue is:

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erotische massage lelystad masturberende kutjes

Samen met CDA en D66 importeren wij jihadisten - satanisten en islamitische neuk pooiers! Samen met CDA en D66 importeren wij satanisten - jihadisten en islamitische neuk pooiers. Hogere lasten - meer islam - minder veiligheid - minder  pensioen en bijstand - AOW leeftijd uiteindelijk naar Ze eten schimmelkaas, drinken oude wijn en rijden een auto zonder dak.

En deel de link! Niet dat we er naar luisterden, maar toch. Nauwelijks verholen racisme en seksisme. Niet in Mokum, niet elders. Dat vinden wij niet raar, dat vinden wij bijzonder! Of zou Rob dat doen? Onze trouwe kiezers zijn de voeding die kanker zo hard nodig heeft!

Stem Djuri Stoter op 21 maart. Dat allemaal dankzij ons! Wij kiezen voor doen! U ook op 21 maart? Stem liberaal , brutaal van achter anaal! Ongelovig volk zonder cultuur en identiteit!

Onze winst is binnen want wij hebben  geld en drugshonden! Doe normaal zoals een liberaal! Gratis zorg en onderdak alleen voor onze asielzoekers!

Ook in moeilijke tijden kunnen graaien bij zieken - werklozen en bejaarden! In moeilijke tijden kunnen graaien bij zieken - armen  bejaarden! Wij verkozen vuiligheid boven eerlijkheid!

Halbe waarheden zul je bedoelen! Onze asielzoekers verdacht want wij importeren en uw dochters mogen creperen! Rot dan maar op. Dat vinden wij beter voor onze toekomst. Nederland tot shithole omtoveren! Dan willen wij heel graag met jou kennismaken! U in een doos op straat en  uitkering van Amalia euro per dag blijft overeind! Tot zeker het jaar blijven wij de grootste! De bijstand - wajong - AOW en pensioen zijn straks verleden tijd! Samen met CDA en D66 gaan wij verder met de afbraak! Sommige mensen zijn meer gelijk dan anderen.

Ook die van de ffd van Rute!!! En nu allemaal 4 jaar je bek houden! Dan heb je pech want bij VVD is alles schrappen heel normaal.

Dan zoek je het zelf maar allemaal lekker uit. Of je nou bankier, CEO of dame van 81 bent. Daar kan ik me weinig meer van herinneren. Dus doe maar een kwartje. En een beetje ket.

Meer geld voor de elite. Er zijn belangrijkere dingen aan de hand in Nederland: Schouder aan schouder de hardwerkende arbeider bevrijden. We gaan met de hamer der verzetting de glazen inslaan van de spiegel der onderdrukking.

Bier, Moppen, en telefoons. En natuurlijk volhouden 'dat alles netjes geregeld is'. Ja hoor vraag maar aan marktlakei Kamp. Die heeft vooral schijt aan de burger. Geen flauwe grapjes meer! Nou, hahahahaha, ik weet het niet, hahahaha. Weet jij niet waarom, dan weet zij het wel. Humorloos, Lange tenen, Normen en Waarden arm. Zorg zonder eigen risico. Pensioen heb ik zelf gespaard! 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 et 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. The first set is derived from the tokenizer output, and can be viewed as a kind of normalized character n-grams. Normalized 1-gram About features. Normalized 3-gram About 36K features. Normalized 4-gram About 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 preferred more focus and restricted ourselves to three systems.

Our primary choice for classification was the use of Support Vector Machines, viz. We chose Support Vector Regression ν-svr to be exact with an RBF kernel, as it had shown the best results in several research projects e. With these main choices, we performed a grid search for well-performing hyperparameters, with the following investigated values: The second classification system was Linguistic Profiling LP; van Halteren , which was specifically designed for authorship recognition and profiling.

Roughly speaking, it classifies on the basis of noticeable over- and underuse of specific features. Before being used in comparisons, all 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 numerical, we used IB1 with k equal to 5 so that we can derive 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 standard deviation. However, the high 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 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 usually , as there are authors , using 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, we let the control shell choose them automatically in a grid search procedure, based on development data.

When running the underlying systems 7. As scaling 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 selection, 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 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 classes 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, we 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 settings. It normalized these by expressing them as the number of non-model class standard 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 development 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 overall results of the gender recognition. We start with the accuracy of the various features and systems Section 5. 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 development data.

Below, in Section 5. Starting with the systems, we see that SVR using original vectors 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 0. 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 the tokens n-grams, it would seem that the further one goes away from the unigrams, the worse the accuracy gets. An explanation for this 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 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 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-grams , will 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. However, we cannot conclude that what is wiped away 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 treated 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 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 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 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 2 , we see much the same picture, although there are differences in the details.

SVR now already reaches its peak TiMBL peaks a bit later at with And LP just mirrors its behaviour with unigrams. 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 an 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 the 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 SVR with token unigrams, this being the best performing combination.

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 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 male is author , with a resounding Looking at his texts, we indeed see a prototypical young male Twitter 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 2 , we 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 the other feature types, we see some variation, 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, author , is not as focused as her male counterpart. There is much 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 by the misclassified ones. Taking again SVR on unigrams as our starting point, this group contains 11 males and 16 females.

We show the 5 most Confidence scores for gender assignment with regard to the female and male profiles built by SVR on the basis of token unigrams. The dashed line represents the separation threshold, i.

The dotted line represents exactly opposite scores for the two genders. Top rankingfemales insvr ontokenunigrams, with ranksand scoresforsvr with various feature types. Top Function 9: With one exception author is recognized as male when using trigrams , all 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 will see shortly.

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

However, his Twitter network contains mostly female friends. This apparently colours not only the discussion topics, which might be expected, but 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 at his tweets, we This has also been remarked by Bamman et al.

There is an extreme number of misspellings even for Twitter , which may possibly confuse the systems models. The most 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 by Sargentini, we find two more female authors each using it once , as compared to 21 male authors with up to 9 uses. Apparently, in our sample, politics is a male thing. We did a quick spot check with author , a 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 have another look at this phenomenon. Are they mostly targeting the content of the tweets, i.

In this section, we will attempt to get closer to the answer to this question. Again, we 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 is impossible to determine which features do the most work.

Instead, we will just look at the distribution of the various 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. The 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 value and the percentages are generally correlated, the bigger tokens are found further away from the diagonal, while the area close to the diagonal contains mostly 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 more negative emotions, such as haat hate and pijn pain. Next we see personal care, with nagels nails , nagellak nail polish , makeup makeup , mascara mascara , and krullen curls.

Clearly, shopping is also important, as is watching soaps on television gtst. The age is reconfirmed by the endearingly high presence of mama and papa. As for style, the only real factor is 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 well. An alternative hypothesis was that Sargentini does not write her own tweets, but assigns this task to a male press spokesperson. However, we received confirmation that she writes almost all her tweets herself Sargentini, personal communication.

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