Dating ping vrienden. Germany: hamburg
Confidence scores for gender assignment with regard to the female and male profiles built by SVR on the basis of token unigrams.
Gender Recognition on Dutch Tweets - PDF
On the female side, everything is less extreme. Feature type Unigram 1: Clearly, shopping is also important, as is watching soaps on television gtst.
For LP, this is by design. 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.
The tokenizer counts on clear markers for these, e. Figure 4 shows that the male population contains some more extreme exponents than the female population.
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. Identity disclosed with permission. The most obvious male is authorwith a resounding Looking at his texts, we indeed see a prototypical young male Twitter user: Recognition accuracy as a function of the number of principal components provided to the systems, using token bigrams.
Apparently, in our sample, politics is a male thing. When adding more information sources, such as profile fields, they reach an accuracy of The control shell then weighted each score by multiplying it by the class separation value on the development data for Dating ping vrienden settings in question, and derived the final score by averaging.
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. We start with the accuracy of the various features and systems Section 5.
However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata. Feature type Unigram Bigram Trigram Skipgram Char 5-gram Top Function 14 get the impression that Dutch is not his native language, which is supported by his name.
This number was treated as just another hyperparameter to be selected. From each user s tweets, we removed all retweets, as these did not contain original text by the author.
And TiMBL is currently underperforming, but might be a challenger to SVR when provided with a better hyperparameter selection mechanism. If, in any application, unbalanced collections are expected, the effects of biases, and corrections for them, will have to be investigated. 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.
Trigrams Three adjacent tokens. 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.
Recognition accuracy as a function of the number of principal components provided to the systems, using normalized character 5-grams. However, looking at SVR is not an option here.
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