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Then we explain how we used the three selected machine learning systems to classify the authors Section 4.
Apart from normal tokens like words, numbers and dates, it is also able to recognize a wide variety Deserve scholarship essay emoticons.
From each user s tweets, we removed all retweets, as these did not contain original text by the author. Normalized 5-gram About K features.
Figures 1, 2, and 3 show accuracy measurements for the token unigrams, token bigrams, and normalized character 5-grams, for all three systems at various numbers of principal components. 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.
The ones used more by women are plotted in green, those used more by men in red. Top rankingfemales insvr ontokenunigrams, with ranksand scoresforsvr with various feature types.
For the measurements with PCA, the number of principal components provided to the classification system is learned from the development data. We represent this quality by the class separation value that we described in Section 4. The age is reconfirmed by the endearingly high presence of mama and papa.
The second set of character n-grams is derived from the original tweets. In the following sections, we first present some previous work on gender recognition Section 2. Feature type Unigram 1: In later research, when we will try to identify the various user types on Twitter, we will certainly have another look at this phenomenon.
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, i. When we look at his tweets, we see a kind of financial blog, which is an exception in the population we have in our corpus.
This meant that, if we still wanted to use k-nn, we would have to reduce the dimensionality of our feature vectors.
However, as research shows a higher number of female users in all as well Heil and Piskorskiwe do not view this as a problem. Interestingly, it is SVR that degrades at higher numbers of principal components, while TiMBL, said to need fewer dimensions, manages to hold on to the recognition quality.
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