PREDICTIONS OF SIX PERSONALITY CHARACTERS (HEXACO) FROM SOCIAL MEDIA USING RANDOM FOREST CLASSIFIER AND PARTICLE SWARM OPTIMIZATION

Personality assessment is important to assess whether a person would be appropriate for a position in accordance to his/her personality. Traditionally, personality assessment is evaluated based on Myers�Briggs Type Indicator. However, this model is considered as too rigid and dichotomous. In this study, we used HEXACO model for personality characterization based on their social media comments and textual interactions. We clean these data by cleaning stopwords and interpreting the emoticon. Then, the features is extracted using TF-IDF. The features then classified using Random Forest Classifier which is optimized by Particle Swarm Optimization. The results show that the accuracy can reach up to 90%. This shows that personality assessment can be performed in a more convenient manner based on the applicant s social media comments and interactions (not necessarily has to be conducted based on traditional personality assessment test).

1.  UEU-Journal-19176-11_0950.pdf - 848 KB

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