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Machine Learning for Personality Type Classification on Textual Data
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DOI：10.1007/978-3-031-61140-7_26.png)
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Abstract
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The Myers-Briggs Type Indicator (MBTI) is typifies personality on the basis of four basic dichotomy traits. It has been used by psychologists with diverse applications in real life and clinical settings. Recently there are attempts to carry out MBTI indexing by Machine Learning (ML) techniques applied to several kinds of signals among them textual data extracted from interactions in social networks. In this paper we apply a battery of well known ML approaches to the prediction of MBTI categories based on features extracted by natural language processing (NLP) techniques from textual data extracted from a social network devoted to personality evaluation. The results are in agreement with the literature, showing that prediction of MBTI personality indicator is highly reproducible.
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