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Gender Recognition on Dutch Tweets - PDF

When adding more information sources, such as profile fields, they reach an accuracy of When looking at his tweets, we They report an overall accuracy of Then we explain how we used the three selected machine learning systems to classify the authors Section 4.

For our experiment, we selected 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.

This meant that, if we still wanted to use k-nn, we would have to reduce the dimensionality of our feature vectors. Here the grid search investigated: We will only look at the final scores for each combination, and forgo the extra detail of any underlying separate male and female model scores Bnn dating gehandicapten gemist we have for SVR and LP; see above.

Dating website single parents exception also leads to more varied classification by the different systems, yielding a wide range of scores.

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. 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.

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LP peaks much earlier In addition, the recognition is of course also influenced by our particular selection of authors, as we will see shortly.

After this, we examine the classification of individual authors Section 5. These percentages are presented below in Section Profiling Strategies In this section, we describe the strategies that we investigated for the gender recognition task.

Normalized 3-gram About 36K features. Again, we take the token unigrams as a starting point. 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.

If, in any application, unbalanced collections are expected, the effects of biases, and corrections for them, will have to be investigated.

Even the character 5-grams have ranks up to 40 for this top 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.

TiMBL peaks a bit later at with This means that the content of the n-grams is more important than their form. We then progressed to the selection of individual users.