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
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5306–5316
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.472
- DOI:
- 10.18653/v1/2020.acl-main.472
- Cite (ACL):
- Veronica Lynn, Niranjan Balasubramanian, and H. Andrew Schwartz. 2020. Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5306–5316, Online. Association for Computational Linguistics.
- Cite (Informal):
- Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention (Lynn et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2020.acl-main.472.pdf