The Interdependency of the Diction and MBTI Personality Type of Online Users

Seoyoon Choi

doi:doi:10.11648/j.ajap.20211001.14

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The Interdependency of the Diction and MBTI Personality Type of Online Users

Seoyoon Choi

Published in American Journal of Applied Psychology (Volume 10, Issue 1)

Received: 25 January 2021 Accepted: 25 February 2021 Published: 3 March 2021

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Abstract

This paper offers insight into the 16 Myers-Briggs Type Indicator (MBTI) personality types and how they may affect the diction used by online users on social media platforms such as Twitter and YouTube. The Myers-Briggs Type Indicator categorizes individuals who take the indicator test into one of 16 different personality types, and each of these types have distinct characteristics, from the simple Introverted versus Extraverted to Intuitive or Sensing, Feeling or Thinking, and Judging or Perceiving. These 4 sets of binary characteristics produce 16 different personalities that are often used to create general pictures or summaries about the individual who was assigned a certain personality type. The characteristics can, on occasion, even predict the potential actions of the individual based on their assigned personality type. This is what allows for the objective of this paper to be achieved - to use data analysis and machine learning to identify the number of times certain words were used by those of different personalities on online platforms, find patterns, and observe if the mechanic prediction of MBTI type based on words used in online posts is possible. The three machine-learning algorithms used to predict the personality types were the Naive Bayes, Gradient, and Random Forest algorithms, with a randomly-selected 80% of the data being used to train the algorithms and the remaining 20% being used to test the machine-learning for accuracy and specificity. This paper will analyze 433,750 total individual posts made online, along with the programming-processed data and the final results of the predictions, identifying which algorithm was most effective in predicting MBTI type and what future steps could be taken to increase accuracy and capacity.

Published in	American Journal of Applied Psychology (Volume 10, Issue 1)
DOI	10.11648/j.ajap.20211001.14
Page(s)	21-26
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Myers-Briggs Type Indicator, Personality Types, Data Analysis, Machine Learning

References

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[2]	“Myers Briggs Personality Types.” Myers Briggs Personality Types - Introduction and Overview, www.teamtechnology.co.uk/tt/t-articl/mb-simpl.htm.
[3]	G. Boyle “Myers_Briggs Type Indicator (MBTI): Some psychometric limitations” Bond University.
[4]	Cherry, Kendra. “Myers-Briggs Type Indicator: The 16 Personality Types.” Verywell Mind, 17 Sept. 2020.
[5]	Riggio, Ronald E. “The Truth About Myers-Briggs Types.” Psychology Today, Sussex Publishers, 21 Feb. 2014.
[6]	Zurcher, Anthony. “Debunking the Myers-Briggs Personality Test.” BBC News, BBC, 15 July 2014.
[7]	W. Wilbur “The automatic identification of stop words”, Journal of Information Science, 1992.
[8]	V. Bala “Stemming and Lemmatization: A comparison of Retrieval Performances”, Lecture notes on Software Engineering 2 (3): 262-267.
[9]	B. Yan, X. Cheng, F. Yang, L. Yao “Research on EDA technology and its related issues”, International Conference On Computer Design and Applications, 2010.
[10]	IBM Cloud Education. “What Is Exploratory Data Analysis?” IBM, www.ibm.com/cloud/learn/exploratory-data-analysis.
[11]	“Personality Types.” 16 Personalities, www.16personalities.com/personality-types.
[12]	K. Hatam, M. Jaf, H. Az and H. Na “How should we report the variation of a study data in a biomedical literature?”, Iranian Journal of Public health, 2018.
[13]	Brownlee, Jason. “How to Encode Text Data for Machine Learning with Scikit-Learn.” Machine Learning Mastery, 27 June 2020.
[14]	“1.9. Naive Bayes.” Scikit Learn, https://scikit-learn.org/stable/modules/naive_bayes.html
[15]	Natekin, Alexey, and Alois Knoll. “Gradient Boosting Machines.” Methods Articles, Frontiers in Neurobotics, 4 December 2013.
[16]	“Random Forests Leo Breiman and Adele Cutler.” Random Forests - Classification Description, www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm.

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APA Style

Seoyoon Choi. (2021). The Interdependency of the Diction and MBTI Personality Type of Online Users. American Journal of Applied Psychology, 10(1), 21-26. https://doi.org/10.11648/j.ajap.20211001.14

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ACS Style

Seoyoon Choi. The Interdependency of the Diction and MBTI Personality Type of Online Users. Am. J. Appl. Psychol. 2021, 10(1), 21-26. doi: 10.11648/j.ajap.20211001.14

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AMA Style

Seoyoon Choi. The Interdependency of the Diction and MBTI Personality Type of Online Users. Am J Appl Psychol. 2021;10(1):21-26. doi: 10.11648/j.ajap.20211001.14

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@article{10.11648/j.ajap.20211001.14,
  author = {Seoyoon Choi},
  title = {The Interdependency of the Diction and MBTI Personality Type of Online Users},
  journal = {American Journal of Applied Psychology},
  volume = {10},
  number = {1},
  pages = {21-26},
  doi = {10.11648/j.ajap.20211001.14},
  url = {https://doi.org/10.11648/j.ajap.20211001.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajap.20211001.14},
  abstract = {This paper offers insight into the 16 Myers-Briggs Type Indicator (MBTI) personality types and how they may affect the diction used by online users on social media platforms such as Twitter and YouTube. The Myers-Briggs Type Indicator categorizes individuals who take the indicator test into one of 16 different personality types, and each of these types have distinct characteristics, from the simple Introverted versus Extraverted to Intuitive or Sensing, Feeling or Thinking, and Judging or Perceiving. These 4 sets of binary characteristics produce 16 different personalities that are often used to create general pictures or summaries about the individual who was assigned a certain personality type. The characteristics can, on occasion, even predict the potential actions of the individual based on their assigned personality type. This is what allows for the objective of this paper to be achieved - to use data analysis and machine learning to identify the number of times certain words were used by those of different personalities on online platforms, find patterns, and observe if the mechanic prediction of MBTI type based on words used in online posts is possible. The three machine-learning algorithms used to predict the personality types were the Naive Bayes, Gradient, and Random Forest algorithms, with a randomly-selected 80% of the data being used to train the algorithms and the remaining 20% being used to test the machine-learning for accuracy and specificity. This paper will analyze 433,750 total individual posts made online, along with the programming-processed data and the final results of the predictions, identifying which algorithm was most effective in predicting MBTI type and what future steps could be taken to increase accuracy and capacity.},
 year = {2021}
}

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TY  - JOUR
T1  - The Interdependency of the Diction and MBTI Personality Type of Online Users
AU  - Seoyoon Choi
Y1  - 2021/03/03
PY  - 2021
N1  - https://doi.org/10.11648/j.ajap.20211001.14
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T2  - American Journal of Applied Psychology
JF  - American Journal of Applied Psychology
JO  - American Journal of Applied Psychology
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PB  - Science Publishing Group
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AB  - This paper offers insight into the 16 Myers-Briggs Type Indicator (MBTI) personality types and how they may affect the diction used by online users on social media platforms such as Twitter and YouTube. The Myers-Briggs Type Indicator categorizes individuals who take the indicator test into one of 16 different personality types, and each of these types have distinct characteristics, from the simple Introverted versus Extraverted to Intuitive or Sensing, Feeling or Thinking, and Judging or Perceiving. These 4 sets of binary characteristics produce 16 different personalities that are often used to create general pictures or summaries about the individual who was assigned a certain personality type. The characteristics can, on occasion, even predict the potential actions of the individual based on their assigned personality type. This is what allows for the objective of this paper to be achieved - to use data analysis and machine learning to identify the number of times certain words were used by those of different personalities on online platforms, find patterns, and observe if the mechanic prediction of MBTI type based on words used in online posts is possible. The three machine-learning algorithms used to predict the personality types were the Naive Bayes, Gradient, and Random Forest algorithms, with a randomly-selected 80% of the data being used to train the algorithms and the remaining 20% being used to test the machine-learning for accuracy and specificity. This paper will analyze 433,750 total individual posts made online, along with the programming-processed data and the final results of the predictions, identifying which algorithm was most effective in predicting MBTI type and what future steps could be taken to increase accuracy and capacity.
VL  - 10
IS  - 1
ER  -

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Author Information

Seoyoon Choi

Seoul International School, Seoul, Republic of Korea

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APA Style

Seoyoon Choi. (2021). The Interdependency of the Diction and MBTI Personality Type of Online Users. American Journal of Applied Psychology, 10(1), 21-26. https://doi.org/10.11648/j.ajap.20211001.14

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ACS Style

Seoyoon Choi. The Interdependency of the Diction and MBTI Personality Type of Online Users. Am. J. Appl. Psychol. 2021, 10(1), 21-26. doi: 10.11648/j.ajap.20211001.14

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AMA Style

Seoyoon Choi. The Interdependency of the Diction and MBTI Personality Type of Online Users. Am J Appl Psychol. 2021;10(1):21-26. doi: 10.11648/j.ajap.20211001.14

Copy | Download

@article{10.11648/j.ajap.20211001.14,
  author = {Seoyoon Choi},
  title = {The Interdependency of the Diction and MBTI Personality Type of Online Users},
  journal = {American Journal of Applied Psychology},
  volume = {10},
  number = {1},
  pages = {21-26},
  doi = {10.11648/j.ajap.20211001.14},
  url = {https://doi.org/10.11648/j.ajap.20211001.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajap.20211001.14},
  abstract = {This paper offers insight into the 16 Myers-Briggs Type Indicator (MBTI) personality types and how they may affect the diction used by online users on social media platforms such as Twitter and YouTube. The Myers-Briggs Type Indicator categorizes individuals who take the indicator test into one of 16 different personality types, and each of these types have distinct characteristics, from the simple Introverted versus Extraverted to Intuitive or Sensing, Feeling or Thinking, and Judging or Perceiving. These 4 sets of binary characteristics produce 16 different personalities that are often used to create general pictures or summaries about the individual who was assigned a certain personality type. The characteristics can, on occasion, even predict the potential actions of the individual based on their assigned personality type. This is what allows for the objective of this paper to be achieved - to use data analysis and machine learning to identify the number of times certain words were used by those of different personalities on online platforms, find patterns, and observe if the mechanic prediction of MBTI type based on words used in online posts is possible. The three machine-learning algorithms used to predict the personality types were the Naive Bayes, Gradient, and Random Forest algorithms, with a randomly-selected 80% of the data being used to train the algorithms and the remaining 20% being used to test the machine-learning for accuracy and specificity. This paper will analyze 433,750 total individual posts made online, along with the programming-processed data and the final results of the predictions, identifying which algorithm was most effective in predicting MBTI type and what future steps could be taken to increase accuracy and capacity.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - The Interdependency of the Diction and MBTI Personality Type of Online Users
AU  - Seoyoon Choi
Y1  - 2021/03/03
PY  - 2021
N1  - https://doi.org/10.11648/j.ajap.20211001.14
DO  - 10.11648/j.ajap.20211001.14
T2  - American Journal of Applied Psychology
JF  - American Journal of Applied Psychology
JO  - American Journal of Applied Psychology
SP  - 21
EP  - 26
PB  - Science Publishing Group
SN  - 2328-5672
UR  - https://doi.org/10.11648/j.ajap.20211001.14
AB  - This paper offers insight into the 16 Myers-Briggs Type Indicator (MBTI) personality types and how they may affect the diction used by online users on social media platforms such as Twitter and YouTube. The Myers-Briggs Type Indicator categorizes individuals who take the indicator test into one of 16 different personality types, and each of these types have distinct characteristics, from the simple Introverted versus Extraverted to Intuitive or Sensing, Feeling or Thinking, and Judging or Perceiving. These 4 sets of binary characteristics produce 16 different personalities that are often used to create general pictures or summaries about the individual who was assigned a certain personality type. The characteristics can, on occasion, even predict the potential actions of the individual based on their assigned personality type. This is what allows for the objective of this paper to be achieved - to use data analysis and machine learning to identify the number of times certain words were used by those of different personalities on online platforms, find patterns, and observe if the mechanic prediction of MBTI type based on words used in online posts is possible. The three machine-learning algorithms used to predict the personality types were the Naive Bayes, Gradient, and Random Forest algorithms, with a randomly-selected 80% of the data being used to train the algorithms and the remaining 20% being used to test the machine-learning for accuracy and specificity. This paper will analyze 433,750 total individual posts made online, along with the programming-processed data and the final results of the predictions, identifying which algorithm was most effective in predicting MBTI type and what future steps could be taken to increase accuracy and capacity.
VL  - 10
IS  - 1
ER  -

Copy | Download