Abstract
Automatic detection of the four MBTI personality dimensions from texts has recently attracted noticeable attention from the natural language processing and computational linguistic communities. Despite the large collections of Twitter data for training, the best systems rarely even outperform the majority-class baseline. In this paper, we discuss the theoretical reasons for such low results and present the insights from an annotation study that further shed the light on this issue.- Anthology ID:
- 2021.eacl-main.312
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3580–3589
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.312
- DOI:
- 10.18653/v1/2021.eacl-main.312
- Cite (ACL):
- Sanja Stajner and Seren Yenikent. 2021. Why Is MBTI Personality Detection from Texts a Difficult Task?. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3580–3589, Online. Association for Computational Linguistics.
- Cite (Informal):
- Why Is MBTI Personality Detection from Texts a Difficult Task? (Stajner & Yenikent, EACL 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.eacl-main.312.pdf