On the Usefulness of Personality Traits in Opinion-oriented Tasks

Marjan Hosseinia, Eduard Dragut, Dainis Boumber, Arjun Mukherjee


Abstract
We use a deep bidirectional transformer to extract the Myers-Briggs personality type from user-generated data in a multi-label and multi-class classification setting. Our dataset is large and made up of three available personality datasets of various social media platforms including Reddit, Twitter, and Personality Cafe forum. We induce personality embeddings from our transformer-based model and investigate if they can be used for downstream text classification tasks. Experimental evidence shows that personality embeddings are effective in three classification tasks including authorship verification, stance, and hyperpartisan detection. We also provide novel and interpretable analysis for the third task: hyperpartisan news classification.
Anthology ID:
2021.ranlp-1.62
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
547–556
Language:
URL:
https://aclanthology.org/2021.ranlp-1.62
DOI:
Bibkey:
Cite (ACL):
Marjan Hosseinia, Eduard Dragut, Dainis Boumber, and Arjun Mukherjee. 2021. On the Usefulness of Personality Traits in Opinion-oriented Tasks. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 547–556, Held Online. INCOMA Ltd..
Cite (Informal):
On the Usefulness of Personality Traits in Opinion-oriented Tasks (Hosseinia et al., RANLP 2021)
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PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2021.ranlp-1.62.pdf