Big Data Classification of Personality Types Based on Respondents’ Big Five Personality Traits

Jennifer Chi

Abstract


A mixed model was introduced in this study, k-means clustering analysis for data examination, discriminant analysis for classification, and multilayer perceptron neural network analysis for prediction. After deleted inadequate samples and outliers, total number of observations was 1,009,998 for this study that was collected through on interactive online personality (i.e., big five personality traits) test in 2018. Empirical results based on the k-means clustering analysis identified four different personality clusters using the total score of big five personality traits (Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness to Experience). Results of the k-means clustering analysis were tested for accuracy using the discriminant analysis indicated that cluster means were significantly different, and showed that 95.8% of original grouped cases correctly classified. The multilayer perceptron neural network framework was utilized as a predictive model, showed a 5-5-4 neural network construction, in deciding the personality classification of participants: Training 99.5% of training grouped cases and 99.5% of testing grouped cases correctly classified. Results of this study may provide insight into the understanding of the personality of participants for further psychological, social, cultural, and economic considerations.

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Keywords


Big Five Personality Traits; Personality Types; Classification; K-means Clustering Analysis; Discriminant Analysis; Multilayer Perceptron Neural Network

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