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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 et al. They report an overall accuracy of Slightly more information seems to be coming from content 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 et al.

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.

With lexical N-grams, they reached an accuracy of Their highest score when using just text features was Although LIWC appears a very interesting addition, it hardly adds anything to the classification.

With only token unigrams, the recognition accuracy was They used lexical features, and present a very good breakdown of various word types. When using all user tweets, they reached an accuracy of 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 of These statistics are derived from the users profile information by way of some heuristics. For gender, the system checks the profile for about common male and 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. 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 tweets.

The age component of the system is described in Nguyen et al. The authors apply logistic and linear regression on counts of token unigrams occurring at least 10 times in their corpus. 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 to represent the social one cf.

Although we agree with Nguyen et al. Experimental Data and Evaluation In this section, we first describe the corpus that we used in our experiments Section 3. Then we outline how we evaluated the various strategies Section 3.

From this material, we considered all tweets with a date stamp in and In all, there were about 23 million users present. This restriction brought the number of users down to aboutWe then progressed to the selection of individual users.

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We aimed for users. We selected of these so that they get a gender assignment in TwiQS, for comparison, but we also wanted to include unmarked users in case these would be different in nature.

All users, obviously, should be individuals, and for each the gender should be clear. From the aboutusers who are assigned a gender by TwiQS, we took a random selection in such a manner that the volume distribution i.

We checked gender manually for all selected users, mostly on the basis 3. As in our own experiment, this measurement is based on Twitter accounts where the user is known to be a human individual.

However, as research shows a higher number of female users in all as well Heil and Piskorskiwe do not view this as a problem. 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 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, i.

URLs and addresses are not completely covered.

flirt smsjes voorbeelden badkamers

The tokenizer counts on clear markers for these, e. 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 to lower case.

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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 4. Then we explain how we used the three selected machine learning systems to classify the authors Section 4. The use of syntax or even higher level features is for 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, 4. Several errors could be traced back to the fact that the account had moved on to another user since We could have used different dividing strategies, but chose balanced folds in order to give a equal chance to all machine learning techniques, also those that have trouble with unbalanced data.

If, in any application, unbalanced collections are expected, the effects of biases, and corrections for them, will have to be investigated. Most of them rely on the tokenization described above. We will illustrate the options we explored with the Hahaha Top Function Words The most frequent function words see kestemont for an overview.

We used the most frequent, as measured on our tweet collection, of which the example tweet contains the words ik, dat, heeft, op, een, voor, and het. Then, we used a set of feature types based on token n-grams, with which we already had previous experience Van Bael and van Halteren For all feature types, we used only those features which were observed with at least 5 authors in our whole collection for skip bigrams 10 authors.

Gender Recognition on Dutch Tweets - PDF

Unigrams Single tokens, similar to the top function words, but then using all tokens instead of a subset. In the example tweet, we find e. Bigrams Two adjacent tokens. In the example tweet, e. Trigrams Three adjacent tokens.

Skip bigrams Two tokens in the tweet, but not adjacent, without any restrictions on the gap size. Finally, we included feature types based on character n-grams following kjell et al.

We used the n-grams with n from 1 to 5, again only when the n-gram was observed with at least 5 authors. However, we used two types of character n-grams. The first set is derived from the tokenizer output, and can be viewed as a kind of normalized character n-grams. Normalized 1-gram About features. Normalized 3-gram About 36K features. Normalized 4-gram About K features.

Normalized 5-gram About K features. The second set of character n-grams is derived from the original tweets. This type of character n-gram has the clear advantage of not needing any preprocessing in the form of tokenization. Original 1-gram About features. Be Original 3-gram About 77K features.

Original 4-gram About K features. Original 5-gram About K features. Again, we decided to explore more than one option, but here we preferred more focus and restricted ourselves to three systems. Our primary choice for classification was the use of Support Vector Machines, viz. With these main choices, we performed a grid search for well-performing hyperparameters, with the following investigated values: The second classification system was Linguistic Profiling LP; van Halterenwhich was specifically designed for authorship recognition and profiling.

Roughly speaking, it classifies on the basis of noticeable over- and underuse of specific features. Before being used in comparisons, all feature counts were normalized to counts per words, and then transformed to Z-scores with regard to the average and standard deviation within each feature.

Here the grid search investigated: As the input features are numerical, we used IB1 with k equal to 5 so that we can derive a confidence value. The only hyperparameters we varied in the grid search are the metric Numerical and Cosine distance and the weighting no weighting, information gain, gain ratio, chi-square, shared variance, and standard deviation.

However, the high dimensionality of our vectors presented us with a problem. For such high numbers of features, it is known that k-nn learning is unlikely to yield useful results Beyer et al.

This meant that, if we still wanted to use k-nn, we would have to reduce the dimensionality of our feature vectors. For each system, we provided the first N principal components for various N. In effect, this N is a further hyperparameter, which we varied from 1 to the total number of components usuallyas there are authorsusing a stepsize of 1 from 1 to 10, and then slowly increasing the stepsize to a maximum of 20 when over Rather than using fixed hyperparameters, we let the control shell choose them automatically in a grid search procedure, based on development data.

When running the underlying systems 7. As scaling is not possible when there are columns with constant values, such columns were removed first. For each setting and author, the systems report both a selected class and a floating point score, which can be used as a confidence score.

In order to improve the robustness of the hyperparameter selection, the best three settings were chosen and used for classifying the current author in question.

For LP, this is by design. A model, called profile, is constructed for each individual class, and the system determines for each author to which degree they are similar to the class profile. For SVR, one would expect symmetry, as both classes are modeled simultaneously, and differ merely in the sign of the numeric class identifier.

However, we do observe different behaviour when reversing the signs. For this reason, we did all classification with SVR and LP twice, once building a male model and once a female model. For both models the control shell calculated a final score, starting with the three outputs for the best hyperparameter settings. It normalized these by expressing them as the number of non-model class standard deviations over the threshold, which was set at the class separation value.

The control shell then weighted each score by multiplying it by the class separation value on the development data for the settings in question, and derived the final score by averaging. It then chose the class for which the final score is highest. In this way, we also get two confidence values, viz. Results In this section, we will present the overall results of the gender recognition. We start with the accuracy of the various features and systems Section 5.

Then we will focus on the effect of preprocessing the input vectors with PCA Section 5. After this, we examine the classification of individual authors Section 5.

For the measurements with PCA, the number of principal components provided to the classification system is learned from the development data. Below, in Section 5. Starting with the systems, we see that SVR using original vectors consistently outperforms the other two.

For only one feature type, character trigrams, LP with PCA manages to reach a higher accuracy than SVR, but the difference is not statistically significant. For SVR and LP, these are rather varied, but TiMBL s confidence value consists of the proportion of selected class cases among the nearest neighbours, which with k at 5 is practically always 0.

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