When using all user tweets, they reached an accuracy of 88.0%.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%.2004), with and without preprocessing the input vectors with Principal Component Analysis (PCA; (Pearson 1901); (Hotelling 1933)).We also varied the recognition features provided to the techniques, using both character and token n-grams.172 For Tweets in Dutch, we first look at the official user interface for the Twi NL data set, Among other things, it shows gender and age statistics for the users producing the tweets found for user specified searches.These statistics are derived from the users profile information by way of some heuristics.Computational Linguistics in the Netherlands Journal 4 (2014) Submitted 06/2014; Published 12/2014 Gender Recognition on Dutch Tweets Hans van Halteren Nander Speerstra Radboud University Nijmegen, CLS, Linguistics Abstract In this paper, we investigate gender recognition on Dutch Twitter material, using a corpus consisting of the full Tweet production (as far as present in the Twi NL data set) of 600 users (known to be human individuals) over 2011 and We experimented with several authorship profiling techniques and various recognition features, using Tweet text only, in order to determine how well they could distinguish between male and female authors of Tweets.
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.
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).
However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata.
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.