Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features

August 22, 2019 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .idea, LICENSE, README.md, __pycache__, eval.py, eval.pyc, main.py, model.py, model.pyc, modelDeleter.py, parameters.py, parameters.pyc, parameters_ar.py, parameters_en.py, parameters_es.py, preprocess.py, preprocess.pyc, run.sh, test.py, train.py, train.pyc, visualizer.py

Authors Erhan Sezerer, Ozan Polatbilek, Selma Tekir arXiv ID 1908.09919 Category cs.CL: Computation & Language Cross-listed cs.LG, stat.ML Citations 1 Venue arXiv.org Repository https://github.com/Darg-Iztech/gender-prediction-from-tweets ⭐ 1 Last Checked 2 months ago

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.