A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts

Published:

Fei Liu, Julien Perez and Scott Nowson (2016) A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts, In Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media, Osaka, Japan, pp. 20-29.

@InProceedings{Liu+:2016,
  author    = {Liu, Fei  and  Perez, Julien  and  Nowson, Scott},
  title     = {A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts},
  booktitle = {Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media},
  year      = {2016},
  address   = {Osaka, Japan},
  pages     = {20--29}
}

Abstract

Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits compared with prior work. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.