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

Fei Liu, Julien Perez, Scott Nowson


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.
Anthology ID:
W16-4303
Volume:
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
20–29
URL:
https://www.aclweb.org/anthology/W16-4303
DOI:
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