Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum
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 su...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-06-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/11382.pdf |
| _version_ | 1797424366693122048 |
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| author | En Jun Choong Kasturi Dewi Varathan |
| author_facet | En Jun Choong Kasturi Dewi Varathan |
| author_sort | En Jun Choong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T08:01:19Z |
| format | Article |
| id | doaj.art-039619aaa87e48b5a99090b20735fbaa |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T08:01:19Z |
| publishDate | 2021-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-039619aaa87e48b5a99090b20735fbaa2023-12-03T00:42:06ZengPeerJ Inc.PeerJ2167-83592021-06-019e1138210.7717/peerj.11382Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forumEn Jun Choong0Kasturi Dewi Varathan1Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaThe 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.https://peerj.com/articles/11382.pdfMyers-Briggs Type IndicatorMBTIPersonality ComputingJudging-PerceivingLight Gradient BoostingNatural Language Processing |
| spellingShingle | En Jun Choong Kasturi Dewi Varathan Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum PeerJ Myers-Briggs Type Indicator MBTI Personality Computing Judging-Perceiving Light Gradient Boosting Natural Language Processing |
| title | Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum |
| title_full | Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum |
| title_fullStr | Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum |
| title_full_unstemmed | Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum |
| title_short | Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum |
| title_sort | predicting judging perceiving of myers briggs type indicator mbti in online social forum |
| topic | Myers-Briggs Type Indicator MBTI Personality Computing Judging-Perceiving Light Gradient Boosting Natural Language Processing |
| url | https://peerj.com/articles/11382.pdf |
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