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
- Editors:
- Malvina Nissim, Viviana Patti, Barbara Plank
- Venue:
- PEOPLES
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 20–29
- Language:
- URL:
- https://aclanthology.org/W16-4303
- DOI:
- Cite (ACL):
- 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 (PEOPLES), pages 20–29, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts (Liu et al., PEOPLES 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W16-4303.pdf