Sommige mensen hebben overigens nergens last van, en de mate van huidirritatie verschilt vaak per gebied. Bovendien geldt dat vrijwel iedere ontharingsmethode huidirritatie met zich kan meebrengen. Hoe kun je epileren? Je kunt op verschillende manieren epileren. Een aantal van de populairste methodes zijn:. Epileren met een pincet, door middel van een pincet kun je fijne haartjes vastpakken en vervolgens met een snelle beweging epileren. Epileren met een pincet is vooral geschikt voor kleine stukjes.

beenhaar epileren je huid groeien in plaats van gewoon naar boven, en dit noemt men ingegroeide haartjes. Door ingegroeide haartjes raakt de omliggende huid vaak ontstoken. Huidirritatie door epileren, epileren is een vrij drastische ontharingsmethode en kan dan ook de nodige huidirritatie opleveren.

Wat zijn de voordelen van epileren? Epileren brengt een aantal belangrijke voordelen met zich mee: haartjes blijven langer weg bij epileren. Het belangrijkste voordeel van epileren ten opzichte van scheren, is dat de haartjes bij epileren veel langer wegblijven. Dit komt omdat je porien de haartjes met wortel en al verwijdert, terwijl je bij scheren slechts het zichtbare (bovenhuidse) gedeelte van de haartjes verwijdert. Zodoende blijven haartjes nadat je ze geëpileerd hebt minimaal zon 3 weken weg, terwijl je bij scheren vaak na een dag alweer stoppels hebt. Door te epileren hoef je dus minder vaak je lichaamshaar te onderhouden. Geen stoppels door epileren, een ander voordeel van epileren is dat haartjes niet als harde stoppels teruggroeien, maar juist als zachtere haartjes. Epileren geeft dus vaak een langdurig(er) glad resultaat. Wat zijn de nadelen van epileren? Eerlijk is eerlijk; epileren kent ook de nodige nadelen: Epileren doet pijn, allereerst kan epileren behoorlijk pijnlijk zijn. Je trekt haartjes namelijk letterlijk uit hun haarzakjes, en dat kan in sommige gevallen flink pijn doen.

beenhaar epileren

Wat is epileren, voordelen, nadelen hoe epileren

Epileren is een populaire techniek om ongewenst lichaamshaar te stockists verwijderen. Vooral meisjes en vrouwen epileren hun lichaamshaar op bepaalde plaatsen. Denk daarbij bijvoorbeeld aan je wenkbrauwen, je benen en je bikinilijn. In dit artikel lees je het antwoord op de volgende vragen: Wat is epileren? Epileren is een ontharingsmethode waarbij clinicas je lichaamshaar met haarwortel en al verwijdert. Dit in tegenstelling tot veel andere ontharingsmethodes (scheren, ontharingscrème. waarbij je alleen het zichtbare (bovenhuidse) gedeelte van de haartjes verwijdert. Kortom: epileren is een grondige methode voor lichaamsontharing.

Benen scheren, harsen of epileren?

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).

beenhaar epileren

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. 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 haar 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.

Beenhaar : definition of, beenhaar and synonyms of, beenhaar (Dutch)

2006 containing about 700,000 posts to m (in total about 140 million words) by almost 20,000 bloggers. 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.

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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. 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 laadt 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 nivea 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.

beenhaar epileren

Eerste indruk pijnloos epileren?!

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 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).


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 rituals 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). 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 symptomen 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, obviously, it is unknown to which degree the information that is present is true.

Beenhaar epileren
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