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'cunt' was used medically by lanfranc, who, in the early 15th century, wrote: "In wymmen the neck of the bladdre is schort, and is maad fast to the cunte" (14-). "Classics in infectious diseases. "Botulinum toxin injections for adults with overactive bladder syndrome". "Abnormal skin irritancy in atopic dermatitis and in atopy without dermatitis". 'cono' and its derivatives are practically ubiquitous in the Spanish language, as Stephen Burgen explains: "People are often shocked at the shear quantity of conos in Spanish discourse" (1996). "Home Of Tony yayo's Mother Sprayed With Bullets".female equivalent" (1999). "I had a lot of pride for Shalimar Parfum Initial says Thierry wasser. "Great Western Spurns Ahmanson, courts rival suitors". "Hyperhidrosis: evolving therapies for a well-established phenomenon".
"Botox chemical may spread, health Canada confirms". "Botulism, botulinum arden Toxin, and bioterrorism: review and Update". "His tenure has led to an atmosphere where citizens know they can come in for assistance that bigger governments may not be able to tend.". "Frontier Federal a deal With Washington Mutual". 'fuck' is used only once in films with 'pg-13' classifications and, likewise, 'cunt' is gaining mainstream acceptance through single appearances in many film scripts. "Actavis plc is now Allergan plc". "Botulinum toxin in the treatment of strabismus. "Botulinum toxin therapy of eye muscle disorders. "Endoscopic approaches to treatment of achalasia". 'is feet will not touch the ground" (2005). "Historical aspects of botulinum toxin: Justinus Kerner (17861862) and the "sausage poison".
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"Botox and clinicas the gummy smile". "Bluntly spelling out the word cunt rather than attempting to symbolise it in visual form" (Amelia jones, 1995) is Marlene McCarty's word-painting Untitled (cunt) (1990 a technique later employed by sam taylor-wood for Cunt (a gothic word-painting, 1996 fiona banner for Concrete poetry cunt' and. "How to contour For your Face Shape". "Genetic diversity within Clostridium botulinum Serotypes, botulinum neurotoxin Gene Clusters and Toxin Subtypes". "I love the fragrance, but I think the whole concept didn't work. "Botox maker bought for 66 billion in biggest deal of 2014". 'cono' is significantly milder than its English equivalent, 'cunt and therefore closely mirrors the similarly mild and omnipresent French term 'con' (of which more later). 'the feminisation of the monstrous (2004).
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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.
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 of misclassified users who have a majority of opposite gender users in their social network. When adding more information sources, such as henriksen profile fields, they reach an accuracy.0. 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.
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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.
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The identification of author traits like gender, age and geographical background. In this beans 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 bloggers. For each blogger, 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 a subtask in remedies 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, 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 tattoo 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 opklaring 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.