Details

Autor(en) / Beteiligte

• Sezerer, Erhan
• Polatbilek, Ozan
• Tekir, Selma

Titel

Gender Prediction from Turkish Tweets with Neural Networks

Ist Teil von

• 2019 27th Signal Processing and Communications Applications Conference (SIU), 2019, p.1-4

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Link zum Volltext

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Author profiling is the characterization of an author through some key attributes such as gender, age, and language. It's an indispensable task especially in security and marketing. In this work, the gender of a Twitter user is predicted using his/her tweets. A model combining a recurrent neural network (RNN) with an attention mechanism is proposed. As far as we know such a predictive analytics is performed in Turkish Twitter dataset for the first time, and the proposed model is tested in Turkish, English, Spanish, and Arabic with accuracy scores of 80.63, 81.73, 78.22, 78.5 respectively. The accuracy values obtained exhibit state-of-the-art in Turkish and competitive performance in the other languages.