Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®

Amirhosseini, Mohammad Hossein; Kazemian, Hassan B.

doi:10.3390/mti4010009

Cited by 91 publications

(43 citation statements)

References 12 publications

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“…Several approaches have been reported in the cognitive science domain regarding the MBTI taxonomy. It should be noted that the results presented here regarding the distribution of the MBTI permutations are similar to those reported in Reference [83]. Moreover, the computational results agree with the principles for cognitive tools set forth in Reference [75].…”

supporting

confidence: 89%

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

et al. 2020

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Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers-Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners.

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confidence: 89%

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

et al. 2020

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“…There are two types of personality for each dimension. This study predicted the personality type of a person based on the MBTI [11]. In the pre-processing stage, they collected data from an Internet forum and removed the MBTI types by using NLTK.…”

Section: E Machine Learning Approach To Personality Typementioning

confidence: 99%

“…They developed three machine learning algorithms, namely Support Vector Machine (SVM), LASSO and Logistic Regression to predict Big Five Personality model. Mohammad Hossein Amirhosseini conducted a study on Machine Learning Approach to Personality Type Prediction Based on the Myers-Briggs Type Indicator on 2020 [11]. The study developed a new machine learning method for automating the process of meta programme detection and personality type prediction based on the MBTI personality type indicator [11].…”

Section: Introductionmentioning

confidence: 99%

“…Mohammad Hossein Amirhosseini conducted a study on Machine Learning Approach to Personality Type Prediction Based on the Myers-Briggs Type Indicator on 2020 [11]. The study developed a new machine learning method for automating the process of meta programme detection and personality type prediction based on the MBTI personality type indicator [11]. The natural language processing toolkit (NLTK) and XGBoost, which is based on Gradient Boosting library in Python is used for implementing machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier

Abidin¹,

Akmal²,

Mohd³

et al. 2020

IJACSA

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The term "personality" can be defined as the mixture of features and qualities that built an individual's distinctive characters, including thinking, feeling and behaviour. Nowadays, it is hard to select the right employees due to the vast pool of candidates. Traditionally, a company will arrange interview sessions with prospective candidates to know their personalities. However, this procedure sometimes demands extra time because the total number of interviewers is lesser than the total number of job seekers. Since technology has evolved rapidly, personality computing has become a popular research field that provides personalisation to users. Currently, researchers have utilised social media data for auto-predicting personality. However, it is complex to mine the social media data as they are noisy, come in various formats and lengths. This paper proposes a machine learning technique using Random Forest classifier to automatically predict people's personality based on Myers-Briggs Type Indicator® (MBTI). Researchers compared the performance of the proposed method in this study with other popular machine learning algorithms. Experimental evaluation demonstrates that Random Forest classifier performs better than the different three machine learning algorithms in terms of accuracy, thus capable in assisting employers in identifying personality types for selecting suitable candidates.

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“…The dataset only contained two columns, one is the user MBTI type, and another is their respective posts on Personality Cafe forum. Five researches on the same dataset were identified, namely Cui & Qi (2017), Bharadwaj et al (2018), Li et al (2018), Mehta et al (2020) and Amirhosseini & Kazemian (2020). The justifications on the reasons of choosing this dataset are as follows:…”

Section: Datasetmentioning

confidence: 99%

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

Choong

Varathan

2021

PeerJ

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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.

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Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®

Cited by 91 publications

References 12 publications

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier

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

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