Gender Recognition on Dutch Tweets
Year: 2014
Type: article
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 TwiNL data set) of 600 users (known to be human individuals) over 2011 and 2012.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.We achieved the best results, 95.5% correct assignment in a 5-fold cross-validation on our corpus, with Support Vector Regression on all token unigrams.Two other machine learning systems, Linguistic Profiling and TiMBL, come close to this result, at least when the input is first preprocessed with PCA.1.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.
Authors: Hans van Halteren, Nander Speerstra
Institutions: Radboud University Nijmegen, Center for Language Studies
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Gender equality
Open Access status: diamond
OpenAlex ID: W2120265183