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( 19 ) door de productie van adiponectine te verhogen, verschaft Raspberry ketone Plus trapsgewijs glucose, om een steiging van de bloedsuiker te voorkomen. #happysaturday #hypnobirthing #zwanger #26weken #zwangerschap #dikkebuik. 10 de twintigste eeuw In de twintigste eeuw kwam er een ware elektronische revolutie op gang. 2 Fink. 13 Another interesting case is author 389. (2012) used svmlight to classify gender on Nigerian twitter accounts, with tweets in English, with a minimum of 50 tweets. #9 Drink Floor haar sapjes Floor is hier helemaal gek van. (dadels kun je goedkoop bij de turkse winkel vinden.).

) Bijwerkingen Er zijn geen meldingen van ernstige bijwerkingen, maar enkele meldingen van milde spijsverteringsproblemen en hoofdpijn. (afstoot uit de pols bij een afstand van 1-1,5 baldikte, vanuit de elleboog bij een wat grotere afstand en snel en indringend afstoten). 10:0 vier prachtige hotties en helemaal voor 3:8 too big butts te voetballen., beter neuken 50:14 te mager voor deze dans 5:47 Mijn latina stap-moeder is ernstig agressief 8:0 haar zoons college maatje michael slapen 8:0 Tags Languages. (Dat komt omdat insuline het enzym hsl blokkeert en hsl is verantwoordelijk voor het mobiliseren van je lichaamsvet.) maar hoe doen we dat nou, voorkomen dat onze bloedsuiker steeds stijgt? 'n Normale ekg is egter nie altyd bepalend nie. 2 Een andere theorie voor het ontstaan van borsten is dat zou worden voorkomen dat de baby stikt tijdens het voeden. 10 The optimal hyperparameter settings are assumed to be those where the two classes are separated most,.

19,99 Direct Bestellen Stephan Pastis isbn flip fiasco schoon genoeg is het vierde deel in de succesvolle graphicnovelreeks Flip fiasco, koper geschreven door Stephan Pastis. #16 de vloeistof waar je niet zonder kan gezond eten gaat hand in hand met gezond drinken. 16 It is intriguing that both here and with the male financial blogger, the erroneous misclassification with unigrams is reversed when using pca on the unigrams. 14 de ruil is een bewonderenswaardige transactie, waarbij beide partijen winnen — altijd! (2014) did a crowdsourcing experiment, in which they asked human participants to guess the gender and age on the basis of 20 to 40 tweets. #10 gezonde snacks.0 hoeveel calorieën denk je dat er in een krop sla zitten? 182 13 Table 3: Top rankingfemales insvr ontokenunigrams, with ranksand scoresforsvr with various feature types. ( 8 ) Conclusie helaas is er maar 1 studie over dit supplement en geen gegevens over de effectiviteit op lange termijn. ( 17, 18 ) Bijwerkingen deze medicatie heeft verschillende bijwerkingen die vooral te maken hebben met de spijsvertering. 15,00, direct Bestellen, scarlett Thomas, isbn, drakendal is het eerste deel in de spannende, magische en humoristische serie bovenwereld van Scarlett Thomas.

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"Iedereen kan tomtom een huis verkopen het lijkt simpel om dat zelf te doen. 13 klontjes suiker in drinkyoghurt zitten zitten? 13,99 Direct Bestellen roald Dahl isbn matilda, de klassieker van roald Dahl, is nu volledig proteinedieet in kleur geïllustreerd door sir quentin Blake. (Juola 2008) and (Koppel. (2014) The effectiveness of breakfast recommendations on weight loss: a randomized controlled trial. ( bron ) Dit is dus een uitstekende manier om het aantal genuttigde calorieën te doen verlagen en snel gewicht te verliezen. ) inacentralposition, butalsocontaining giggling (hihi) and lots of emotionally loaded adjectives, such as lief and lieve ( sweet schattig ( cute leuk and leuke ( nice ). 173 4 of the profile texts and profile photo s, and only included those for which we were convinced of the gender. #12 Wordt een goed geoliede machine ik betrek het liefst geen religie in mijn blogs.

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With lexical N-grams, they reached an accuracy.7, which the combination with the sociolinguistic features increased.33. (2011) attempted to recognize gender in tweets from a whole set of languages, using word and character N-grams as features for machine learning with Support Vector Machines (svm naive bayes and Balanced Winnow2. Their highest score when using just text features was.5, testing on all the tweets by each author (with a train set.3 million tweets and a test set of about 418,000 tweets). 2 Fink. (2012) used svmlight to classify gender on Nigerian twitter accounts, with tweets in English, with a minimum of 50 tweets. Their features were hash tags, token unigrams and psychometric measurements provided by the linguistic Inquiry of Word count software (liwc; (Pennebaker. Although liwc appears a very interesting addition, it hardly adds anything to the classification. With only token unigrams, the recognition accuracy was.5, while using all features together increased this only slightly.6. (2014) examined about 9 million tweets by 14,000 Twitter users tweeting in American English.

This corpus has been used extensively since. The creators themselves used it for vetten various classification tasks, including gender recognition (Koppel. They report an overall accuracy.1. Slightly more information seems to be coming from content (75.1 accuracy) than from style (72.0 accuracy). However, even style appears to mirror content. We see the women focusing on personal matters, leading to important content words like love and boyfriend, and important style words like i and other personal pronouns.

The men, on the other hand, seem to be more interested in computers, leading to important content words like software and game, and correspondingly more determiners and prepositions. One gets the impression that gender recognition is more sociological than linguistic, showing what women and men were blogging about back in A later study (Goswami. 2009) managed to increase the gender recognition quality.2, using sentence length, 35 non-dictionary words, and 52 slang words. The authors do not report the set of slang words, but the non-dictionary words appear to be more related to style than to content, showing that purely linguistic behaviour can contribute information for gender recognition as well. Gender recognition has also already been applied to Tweets. (2010) examined various traits of authors from India tweeting in English, combining character N-grams and sociolinguistic features like manner of laughing, honorifics, and smiley use.

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In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section. A group which is very active in studying gender recognition (among other traits) on the basis of text is that around Moshe koppel. In (Koppel. 2002) they report gender recognition on formal written texts taken from the British National Corpus (and also give a good overview of previous work reaching about 80 correct attributions using function words and parts of speech. Later, in 2004, the group collected a blog Authorship Corpus (BAC; (Schler. 2006 containing about 700,000 posts to m (in total about 140 million words) by almost 20,000 wallen bloggers. For each blogger, grote metadata is present, including the blogger s self-provided gender, age, industry and astrological sign.

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Then follow the results (Section 5 and Section 6 concludes the paper. For whom we already know that they are an individual person rather than, say, a husband and wife couple or a board of editors for an official Twitterfeed. C 2014 van Halteren and Speerstra. Gender Recognition Gender recognition is recepten a subtask in the general field of authorship recognition and profiling, which has reached maturity in the last decades(for an overview, see. (Juola 2008) and (Koppel. Currently the field is getting an impulse for further development now that vast data sets of user generated data is becoming available. (2012) show that authorship recognition is also possible (to some degree) if the number of candidate authors is as high as 100,000 (as compared to the usually less than ten in traditional studies). Even so, there are circumstances where outright recognition is not an option, but where one must be content with profiling,. The identification of author traits like gender, age and geographical background.

The resource would become even more useful if we could deduce complete and correct metadata from the various available information sources, such as the provided metadata, user relations, profile photos, and the text of the tweets. In this paper, we start modestly, by attempting to derive just the gender of the authors 1 automatically, purely on the basis of the content of their tweets, using author profiling techniques. For our experiment, we selected 600 authors for whom we were able to determine with pijn a high degree of certainty a) that they were human individuals and b) what gender they were. We then experimented with several author profiling techniques, namely support Vector Regression (as provided by libsvm; (Chang and Lin 2011 linguistic Profiling (LP; (van Halteren 2004 and timbl (Daelemans. 2004 with and without preprocessing the input vectors with Principal Component Analysis (PCA; (Pearson 1901 (Hotelling 1933). We also varied the recognition features provided to the techniques, using both character and token n-grams. For all techniques and features, we ran the same 5-fold cross-validation experiments in order to determine how well they could be used to distinguish between male and female authors of tweets. In the following sections, we first present some previous work on gender recognition (Section 2). Then we describe our experimental data and the evaluation method (Section 3 after which we proceed to describe the various author profiling strategies that we investigated (Section 4).

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1 Computational Linguistics in the netherlands journal 4 (2014) Submitted 06/2014; Published 12/2014 Gender Recognition on Dutch Tweets Hans van Halteren Nander Speerstra radboud University nijmegen, cls, linguistics Abstract In this paper, we investigate gender recognition on Dutch Twitter material, using a corpus consisting. We achieved the best results,.5 correct assignment in a 5-fold cross-validation on our corpus, with Support Vector Regression on all token unigrams. Two other machine learning systems, linguistic Profiling and timbl, come close to this result, at least when the input is first preprocessed with pca. Introduction In the netherlands, we have a rather unique resource in the form of the Twinl data set: a daily updated collection that probably contains at least 30 of the dutch public tweet production since 2011 (Tjong Kim Sang and van den Bosch 2013). However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata. In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields. And, vandaag obviously, it is unknown to which degree the information that is present is true.

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