A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts

Published:

Fei Liu, Julien Perez and Scott Nowson (2017) A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts, In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, pp. 754-764.

@InProceedings{Liu+:2017,
  author    = {Liu, Fei  and  Perez, Julien  and  Nowson, Scott},
  title     = {A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics},
  year      = {2017},
  address   = {Valencia, Spain},
  pages     = {754--764}
}

Abstract

There have been many attempts at automatically recognising author personality traits from text, typically incorporating linguistic features with conventional machine learning models, e.g. linear regression or Support Vector Machines. In this work, we propose to use deep-learning-based models with atomic features of text – the characters – to build hierarchical, vectorial word and sentence representations for the task of trait inference. On a corpus of tweets, this method shows state-of-the-art performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.