article · IEEE Access · 2021

A Method for MBTI Classification Based on Impact of Class Components

Ninoslav Čerkez, Boris Vrdoljak, Sandro Skansi

Cited by 31Open access

Abstract

Predicting the personality type of text authors has a well-known usage in psychology with practical applications in business. From the data science perspective, we can look at this problem as a text classification task that can be tackled using natural language processing (NLP) and deep learning. This paper proposes a method and a novel loss function for multiclass classification using the Myers–Briggs Type Indicator (MBTI) approach for predicting the author’s personality type. Furthermore, this paper proposes an approach that improves the current results of the MBTI multiclass classification because it considers components of compound class labels as supportive elements for better classification according to MBTI. As such, it also provides a new perspective on this classification problem. The experimental results on long short-term memory (LSTM) and convolutional neural network (CNN) models outperform baseline models for multiclass classification, related research on multiclass classification, and most research with four binary approaches to MBTI classification. Moreover, other classification problems that target compound class labels and label parts with binary mutually exclusive values can benefit from this approach.

Cite this paper

APA 7

Čerkez, N., Vrdoljak, B., & Skansi, S. (2021). A Method for MBTI Classification Based on Impact of Class Components. IEEE Access, 9, 146550–146567. https://doi.org/10.1109/access.2021.3121137

MLA 9

Čerkez, Ninoslav, et al. “A Method for MBTI Classification Based on Impact of Class Components.” IEEE Access, vol. 9, 2021, pp. 146550–67, https://doi.org/10.1109/access.2021.3121137.

Chicago

Čerkez, Ninoslav, Boris Vrdoljak, and Sandro Skansi. 2021. “A Method for MBTI Classification Based on Impact of Class Components.” IEEE Access 9: 146550–67. https://doi.org/10.1109/access.2021.3121137.

Harvard

Čerkez, N., Vrdoljak, B. and Skansi, S. (2021) ‘A Method for MBTI Classification Based on Impact of Class Components’, IEEE Access, 9, pp. 146550–146567. Available at: https://doi.org/10.1109/access.2021.3121137.

BibTeX

@article{cerkez2021method,
  title = {{A Method for MBTI Classification Based on Impact of Class Components}},
  author = {Čerkez, Ninoslav and Vrdoljak, Boris and Skansi, Sandro},
  journal = {IEEE Access},
  volume = {9},
  pages = {146550--146567},
  year = {2021},
  doi = {10.1109/access.2021.3121137}
}

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