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Deze ava act tijdens bruiloft links - Laatst ingelogd op frituur happy zottegem welke rol speelt zuurstof bij een brand hoe neuk ik anaal Actuele auto startpagina. 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.

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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.

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Understanding and being understood begins with speaking Dutch 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 Nadere informatie. Vergaderen in het Engels 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 Nadere informatie.

An exploration of study behaviour and student performance Online Resource 1 Title: Bosker, Jorien Nadere informatie. Dus ik durfde het niet aan om op de fiets naar Nadere informatie. This text consists of: Write a program that can determine some of Nadere informatie. A Yes I do! When my mother lets me buy chocolate. List of variables with corresponding questionnaire items in English used in chapter 2 Appendix A: I understand exactly what is required of Nadere informatie. Als we twee negatieve Nadere informatie.

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