Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features
Computation and Language
2019-09-09 v2 Machine Learning
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1908.09919,
title = {Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features},
author = {Erhan Sezerer and Ozan Polatbilek and Selma Tekir},
journal= {arXiv preprint arXiv:1908.09919},
year = {2019}
}