Gender Recognition on Dutch Tweets - PDF Gender Recognition on Dutch Tweets - PDF

Dating voor hoger opgeleiden ervaringen, improv for programmers: when harddrives attack

Dating profile what am i looking for

In this section, we want to investigate how strong this dependency may have been. This apparently colours not only the discussion topics, which might be expected, but also the general language use.

Russian dating pictures 9gag

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. This corpus has been used extensively since. To test that, we would have to experiment with a new feature types, modeling exactly the difference between the normalized and the original form.

Skip bigrams Two tokens in the tweet, but not adjacent, without any restrictions on the gap size. Possibly, the other n-grams are just mirroring this quality of the unigrams, with the effectiveness of the mirror depending on how well unigrams are represented in the n-grams.

The license may not give you all of the permissions necessary for your intended use.

Normalized 5-gram About K features. Even the character 5-grams have ranks up to 40 for this top From this material, we considered all tweets with a date stamp in and In all, there were about 23 million users present.

Where to buy hook up watch

Attribution — You must give appropriate creditprovide a link to the license, and indicate if changes were made. Instead, we will just look at the distribution of the various features over the female and male texts. In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section.

However, even style appears to mirror content. For the other feature types, we see some variation, but most scores are found near the top of the lists.

This is in accordance with the hypothesis just suggested for the token n-grams, as normalization too brings the character n-grams closer to token unigrams. However, we do observe different behaviour when reversing the signs.

Furthermore, LP appears to suffer some kind of mathematical breakdown for higher numbers of components. We then progressed to the selection of individual users.

Then follow the results Section 5and Section 6 concludes the paper. 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.

You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. 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.

In the example tweet, we find e.

Dating with a chinese guy

The age component of the system is described in Nguyen et al. The unigrams do not judge him to write in an extremely female way, but all other feature types do. This means that the content of the n-grams is more important than their form.

Creative Commons — Attribution Generic — CC BY

The male which is attributed the most female score is author 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 The position in the plot represents the relative number of men and women who used the token at least once somewhere in their tweets.

Apart from the general agreement on the final decision, the feature types vary widely in the scores assigned, but this also allows for both conclusions.

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.

Here the grid search investigated: This meant that, if we still wanted to use k-nn, we would have to reduce the dimensionality of our feature vectors.

An alternative hypothesis was that Sargentini does not write her own tweets, but assigns this task to a male press spokesperson. 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.

Several errors could be traced back to the fact that the account had Diy solar hookup 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.

Rather than using fixed hyperparameters, we let the control shell choose them automatically in a grid search procedure, based on development data. Original 5-gram About K features.