Choong, En Jun and Varathan, Kasturi Dewi (2021) Predicting judging-perceiving of myers-briggs type indicator (MBTI) in online social forum. PeerJ, 9. ISSN 2167-8359, DOI https://doi.org/10.7717/peerj.11382.
Full text not available from this repository.Abstract
The Myers-Briggs Type Indicator (MBTI) is a well-known personality test that assigns a personality type to a user by using four traits dichotomies. For many years, people have used MBTI as an instrument to develop self-awareness and to guide their personal decisions. Previous researches have good successes in predicting Extraversion-Introversion (E/I), Sensing-Intuition (S/N) and Thinking-Feeling (T/F) dichotomies from textual data but struggled to do so with Judging-Perceiving (J/P) dichotomy. J/P dichotomy in MBTI is a non-separable part of MBTI that have significant inference on human behavior, perception and decision towards their surroundings. It is an assessment on how someone interacts with the world when making decision. This research was set out to evaluate the performance of the individual features and classifiers for J/P dichotomy in personality computing. At the end, data leakage was found in dataset originating from the Personality Forum Cafe, which was used in recent researches. The results obtained from the previous research on this dataset were suggested to be overly optimistic. Using the same settings, this research managed to outperform previous researches. Five machine learning algorithms were compared, and LightGBM model was recommended for the task of predicting J/P dichotomy in MBTI personality computing.
| Item Type: | Article |
|---|---|
| Funders: | Impact Oriented Interdisciplinary Research Grant University of Malaya (IIRG001A-19SAH) |
| Uncontrolled Keywords: | Myers-Briggs Type Indicator; MBTI; Personality Computing; Judging-Perceiving; Light Gradient Boosting; Natural Language Processing |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 09 Jun 2022 07:33 |
| Last Modified: | 09 Jun 2022 07:33 |
| URI: | http://eprints.um.edu.my/id/eprint/27576 |
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