  Author profiling is the characterization of an author through some key
attributes such as gender, age, and language. In this paper, a RNN model with
Attention (RNNwA) is proposed to predict the gender of a twitter user using
their tweets. Both word level and tweet level attentions are utilized to learn
'where to look'. This model
(https://github.com/Darg-Iztech/gender-prediction-from-tweets) is improved by
concatenating LSA-reduced n-gram features with the learned neural
representation of a user. Both models are tested on three languages: English,
Spanish, Arabic. The improved version of the proposed model (RNNwA + n-gram)
achieves state-of-the-art performance on English and has competitive results on
Spanish and Arabic.
