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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.
2009) managed to increase the gender recognition quality to 89.2%, using sentence length, 35 non-dictionary words, and 52 slang words.
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.
With only token unigrams, the recognition accuracy was 80.5%, while using all features together increased this only slightly to 80.6%. (2014) examined about 9 million tweets by 14,000 Twitter users tweeting in American English.
Then follow the results (Section 5), and Section 6 concludes the paper. For whom we already know that they are an individual person rather than, say, a husband and wife couple or a board of editors for an official Twitterfeed. the identification of author traits like gender, age and geographical background.
In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section.
In this paper, we start modestly, by attempting to derive just the gender of the authors 1 automatically, purely on the basis of the content of their tweets, using author profiling techniques.
For our experiment, we selected 600 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.
For all techniques and features, we ran the same 5-fold cross-validation experiments in order to determine how well they could be used to distinguish between male and female authors of tweets.
An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network. When adding more information sources, such as profile fields, they reach an accuracy of 92.0%.