Literature DB >> 34221705

Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum.

En Jun Choong1, Kasturi Dewi Varathan1.   

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 Café, 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. ©2021 Choong and Varathan.

Entities:  

Keywords:  Judging-Perceiving; Light Gradient Boosting; MBTI; Myers-Briggs Type Indicator; Natural Language Processing; Personality Computing

Year:  2021        PMID: 34221705      PMCID: PMC8234987          DOI: 10.7717/peerj.11382

Source DB:  PubMed          Journal:  PeerJ        ISSN: 2167-8359            Impact factor:   2.984


  2 in total

1.  Personality Assessment for Employee Development: Ivory Tower or Real World?

Authors:  Penny Moyle; John Hackston
Journal:  J Pers Assess       Date:  2018-06-22

Review 2.  TECLA: A temperament and psychological type prediction framework from Twitter data.

Authors:  Ana Carolina E S Lima; Leandro Nunes de Castro
Journal:  PLoS One       Date:  2019-03-12       Impact factor: 3.240

  2 in total

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