The enhancement of Personality Assessment and Detection using Machine Learning Techniques

DOI

10.2991/978-94-6463-300-9_12How to use a DOI?

Keywords

MBTI; Logistic Regression; SVM; Gradient Boosting

Abstract

The rapid advancements in science and technology have had a profound impact on how people perceive themselves and communicate with others. As a result, personality tests have become increasingly popular for individuals seeking self-awareness and a deeper understanding of others. This study focuses specifically on the Myers-Briggs Type Indicator (MBTI) personality test and utilizes machine learning techniques to enhance its effectiveness. The research begins by exploring various methods for conducting personality tests, including Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting (GB). These methods are compared, and based on their performance, Gradient Boosting is identified as the most promising approach. Further optimization is carried out, resulting in a final model capable of accurately predicting personality traits. The precision and accuracy of the model meet the desired requirements, showcasing its potential for practical applications. Moving forward, the study highlights the importance of future improvements and refinements to enhance the overall performance of the model. By continually advancing and refining machine learning techniques in the context of personality assessment, individuals can gain valuable insights into themselves and others, leading to personal growth and improved communication. In summary, this research demonstrates the significant role of machine learning in improving personality tests like MBTI, empowering individuals to develop self-awareness and foster meaningful connections with others.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shunyu Chen
AU  - Yichen Liu
AU  - Tianqin Meng
AU  - Sibo Wang
PY  - 2023
DA  - 2023/11/27
TI  - The enhancement of Personality Assessment and Detection using Machine Learning Techniques
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 110
EP  - 121
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_12
DO  - 10.2991/978-94-6463-300-9_12
ID  - Chen2023
ER  -