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And, obviously, it is unknown to which degree the information that is present is true.
The tokenizer is able to identify hashtags and Twitter user names to the extent that these conform to the conventions used in Twitter, i. For all feature types, we used only those features which were observed with at least 5 authors in our whole collection for skip bigrams 10 authors.
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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. But it might alsomean that the gender just influences all feature types to a similar degree.
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The authors apply logistic and linear regression on counts of token unigrams occurring at least 10 times in their corpus. This has also been remarked by Bamman et al.
We achieved the best results, However, his Twitter network contains mostly female friends. This type of character n-gram has the clear advantage of not needing any preprocessing in the form of tokenization. From this point on in the discussion, we will present female confidence as positive numbers and male as negative.
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. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
However, we cannot conclude that what is wiped away by the normalization, use of diacritics, capitals and spacing, holds no information for the gender recognition.
Assuming that any sequence including periods is likely to be a URL provesunwise, given that spacing between normal wordsis often irregular. And by TweetGenie as well.
URLs and addresses are not completely covered. You are free to: In this section, we will attempt to get closer to the answer to this question.
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However, even with purely lexical features, 4. Confidence scores for gender assignment with regard to the female and male profiles built by SVR on the basis of token unigrams.
Recognition accuracy as a function of the number of principal components provided to the systems, using normalized character 5-grams.