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In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields.And, obviously, it is unknown to which degree the information that is present is true.Two other machine learning systems, Linguistic Profiling and Ti MBL, come close to this result, at least when the input is first preprocessed with PCA. Introduction In the Netherlands, we have a rather unique resource in the form of the Twi NL data set: a daily updated collection that probably contains at least 30% of the Dutch public tweet production since 2011 (Tjong Kim Sang and van den Bosch 2013).
It might just be because those surveyed by Hall were older, and had been married longer, than those who met elsewhere on the web.
Their highest score when using just text features was 75.5%, testing on all the tweets by each author (with a train set of 3.3 million tweets and a test set of about 418,000 tweets). (2012) used SVMlight to classify gender on Nigerian twitter accounts, with tweets in English, with a minimum of 50 tweets.
Their features were hash tags, token unigrams and psychometric measurements provided by the Linguistic Inquiry of Word Count software (LIWC; (Pennebaker et al. Although LIWC appears a very interesting addition, it hardly adds anything to the classification.
Whether or not my other half exists remains to be seen, Ok..anyone that has been keeping tabs on how thing are going.....
I'm just waiting for the ex to sign the papers for the divorce.