De prop (cerebral sinus venous thrombosis) kan dus ontstaan door het uitknijpen van een doodnormale puist. Nou als deze informatie geen goede rede is om met je smerige vingers van je puisten af te blijven dan weet ik het ook niet meer. Zie dit als een waarschuwing, negeer het en ervaar de gevolgen van de Driehoek des doods!
Lees hier over de gevaren van het uitknijpen van een puist. Er is namelijk een bepaalde zone in je gezicht waarin het levensgevaarlijk is om een rijpe mee-eter uit te knijpen. Bijna elke tiener krijgt last van jeugdpuistjes en acne als de hormonen door het lichaam heen gieren in de pubertijd. Een handjevol volwassenen ervaren ook last van puisten in hun gezicht. Het volgende scenario zal dan ook voor niemand veel vraagtekens oproepen. Op een gegeven moment begin je een bultje op te merken ergens op je gezicht. Misschien zit de verharding op je neus, kin, voorhoofd of naast je lip. Binnen de kortste keren groeit de bult en voordat je het weet zit er een dikke, vieze bult gevuld met witte troep op je gezichtsvlees. Deze rijpe puist wil je maar al te graag zo snel mogelijk uitknijpen, dus hup naar de spiegel toe. Aldaar staar je jezelf aan en vaak voel je jezelf dan lelijk omdat een gave huid een schoonheidsideaal is en die vieze met lichaamsafval gevulde bult zo snel mogelijk moet verdwijnen.
Puisten uitdrukken in nemo amsterdam
Beschikbare opties bij deze post, gerelateerde posts 5409 views door, sneezy op Wed. Rennen, springen, vliegen, vallen en weer creme opstaan ik waarschuw u al tis echt degoutant. Tags: goor, puist, groot, zot. Definition from wiktionary, the free dictionary. Jump to: navigation, search, retrieved from " ". Een puist uitknijpen op je gezicht? Pas op dat je niet sterft! Nee, het onderwerp van dit artikel is alles behalve een broodje aap verhaal. Er zijn jongens en meisjes van jouw leeftijd overleden aan het uitknijpen van een onschuldig ogende puist.
Esmee vermeire on Twitter: @realjosly ik zou jouw puist
172 3 For Tweets in Dutch, we first look at the official user interface for the Twinl data set, Among other things, it shows gender and age statistics for the users producing the tweets found for user specified searches. These statistics are derived from the users profile information by way of some heuristics. For gender, the system checks the profile for about 150 common male and 150 common female first names, as well as for gender related words, such as father, mother, wife and husband. If no cue is found in a user s profile, no gender is assigned. The general quality of the assignment is unknown, but in the (for this purpose) rather unrepresentative sample of users we considered for our own gender assignment corpus (see below we find that about 44 of the users are assigned a gender, which is correct. Another system that predicts the gender for Dutch Twitter users is TweetGenie that one can provide with a twitter user name, after which the gender and age are estimated, based on the user s last 200 tweets. The age component of the system is described in (Nguyen. The authors apply logistic and linear regression on counts of token unigrams occurring at least 10 times in their corpus. The paper does not describe the gender component, but the first author has informed us that the accuracy of the gender recognition on the basis of 200 tweets is about 87 (Nguyen, personal communication).
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 schommelende 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 zonder 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. They used lexical features, and present a very good breakdown of various word types. When using all user tweets, they reached an accuracy.0. An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network. When adding more information sources, such as profile fields, they reach an accuracy.0.
Puisten Uitknijpen - de goede manier
For each blogger, metadata is present, including the blogger s self-provided gender, age, industry and astrological sign. This corpus has been used extensively since. The creators themselves used it for 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. 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.
Een jongen sterft door het uitknijpen van een puist
The identification of author traits like gender, age and geographical background. 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 instapmodel 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 lever words) by almost 20,000 bloggers.
Steenpuist / Furunkel
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). 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 simpel Halteren and Speerstra. Gender Recognition Gender recognition is 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 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, zoutarm 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 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).
Een van de grootste puisten ooit - gwn goor
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 bakken 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, obviously, it is unknown to which degree the information that is present is true.