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For those techniques where hyperparameters need to be selected, we used a leave-one-out strategy on the test material. For each test author, we determined the optimal hyperparameter settings with regard to the classification of all other authors in the same part of the corpus, in effect using these as development material. In this way, we derived a classification score for each author without the system having any direct or indirect access to the actual gender of the author. We then measured for which percentage of the authors in the corpus this score was in agreement with the actual gender. These percentages are presented below in Section Profiling Strategies In this section, we describe the strategies that we investigated for the gender recognition task. As we approached the task from a machine learning viewpoint, we needed to select text features to be provided as input to the machine learning systems, as well as machine learning systems which are to use this input for classification. We first describe the features we used (Section.1). Then we explain how we used the three selected machine learning systems to classify the authors (Section.2). 4.1 Machine learning features we restricted ourselves to lexical features for our experiments. The use of syntax or even higher level features is (for badzout now) impossible as the language use on Twitter deviates too much from standard Dutch, and we have no tools to provide reliable analyses. However, even with purely lexical features,.
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From each user s tweets, we removed all retweets, as these did not contain original text by the author. Then, as several of our features were anti based on tokens, we tokenized all text samples, using our own specialized tokenizer for tweets. Apart from normal tokens like words, numbers and dates, it is also able to recognize a wide variety of emoticons. The tokenizer is able to identify hashtags and Twitter user names to the extent that these conform to the conventions used in Twitter,. At sign are followed by a series of letters, digits and underscores. Urls and addresses are not completely covered. The tokenizer counts on clear markers for these,. Http, www or one of a number of domain names for urls. Assuming that any sequence including periods is likely to be a url provesunwise, given that spacing between normal wordsis often irregular. And actually checking the existence of a proposed url was computationally infeasible for the amount of text we intended to process. Finally, as the use of capitalization and diacritics is quite haphazard in the tweets, the tokenizer strips all words of diacritics and transforms them stoep to lower case. 3.2 evaluation we divided our corpus in five parts, each containing (approximately) the same number of male and female authors. 6 we used this division in all experiments, each time using four parts as training material and one as test material.
these would be different in nature. All users, obviously, should be individuals, and for each the gender should be clear. From the about 120,000 users who are assigned a gender by Twiqs, we took a random selection in such a manner that the volume distribution (i.e. From 2 to 10 tweets per day average) is equally spread throughout the range and approximately equal for men and women. We checked gender manually for all selected users, mostly on the basis. As in our own experiment, this measurement is based on Twitter accounts where the user is known to be a human individual. 173 4 of the profile texts and profile photo s, and only included those for which we were convinced of the gender. 4 Later even more detailed rechecks, after a few extremely unlikely classification results, served to clean up the (hopefully) last gender assignment errors. 5 The final corpus is not completely balanced for gender, but consists of the production of 320 women and 280 men. However, as research shows a higher number of female users in all as well (Heil and Piskorski 2009 we do not view this as a problem.
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Also (Bamman. Although we agree with Nguyen. On this, we will still take the biological gender as the gold standard in this paper, as our nivea eventual goal is creating metadata for the Twinl collection. Experimental Data and evaluation In this section, we first describe the corpus that we used in our experiments (Section.1). Then we outline how we evaluated the various strategies (Section.2). 3.1 Corpus Used in the Experiments we selected our experimental material from the Twinl data set (Tjong Kim Sang and van den Bosch 2013 which was collected by searching for tweets with any of a number of probably dutch words, after which a character n-gram. The collection is estimated to contain 30-40 of all public Dutch tweets. From this material, we considered all tweets with a date stamp in 2011 and In all, there were about 23 million beere users present. Of these, we only considered the ones who produced 2 to 10 tweets on average per day over 2011 and The minimum ensured a sufficient amount of text ( tweets) for classification; the maximum served to avoid very high volume users, who might be professional. This restriction brought the number of users down to about 270,000.
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). 3 In later experiments, Nguyen. (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. When using a majority vote to represent the crowd s opinion, the crowd s perception of the gender on the basis of the tweets coincided with the actual gender in about 84 of the cases. The conclusion is not so much, however, that humans are also not perfect at guessing age on the basis of language use, but rather that there is a distinction between the biological and the social identity of authors, and language use is more likely.
15 Stainless Steel Scalpel, 100 Per Case2 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 mellékhatásai 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. 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.
Slightly more information seems to be coming from content (75.1 accuracy) than dode 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. 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).
Gender Recognition on Dutch Tweets - pdf
Even so, there are circumstances where outright recognition is not an option, but where one must be content with profiling,. The identification of gute 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 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. 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.
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). 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 vervolgopleiding 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).
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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 hauschka 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. 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 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.