Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention

Veronica Lynn, Niranjan Balasubramanian, H. Andrew Schwartz


Abstract
Not all documents are equally important. Language processing is increasingly finding use as a supplement for questionnaires to assess psychological attributes of consenting individuals, but most approaches neglect to consider whether all documents of an individual are equally informative. In this paper, we present a novel model that uses message-level attention to learn the relative weight of users’ social media posts for assessing their five factor personality traits. We demonstrate that models with message-level attention outperform those with word-level attention, and ultimately yield state-of-the-art accuracies for all five traits by using both word and message attention in combination with past approaches (an average increase in Pearson r of 2.5%). In addition, examination of the high-signal posts identified by our model provides insight into the relationship between language and personality, helping to inform future work.
Anthology ID:
2020.acl-main.472
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5306–5316
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.472
DOI:
10.18653/v1/2020.acl-main.472
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.472.pdf
Dataset:
 2020.acl-main.472.Dataset.pdf
Video:
 http://slideslive.com/38929297