Personality Type Based on Myers-Briggs Type Indicator with Text Posting Style by using Traditional and Deep Learning
This is an incremental application of existing methods to personality prediction from text, relevant for psychology or social computing domains.
The paper tackles predicting Myers-Briggs Type Indicator personality types from text using machine learning techniques like Naive Bayes, SVM, and RNNs, and applies CRISP-DM with agile methodology to reduce development cycles, but does not report specific performance numbers.
The term personality may be expressed in terms of the individual differences in characteristics pattern of thinking, feeling, and behavior. This work presents several machine learning techniques including Naive Bayes, Support Vector Machines, and Recurrent Neural Networks to predict people personality from text based on Myers-Briggs Type Indicator (MBTI). Furthermore, this project applies CRISP-DM, which stands for Cross-Industry Standard Process for Data Mining, to guide the learning process. Since, CRISP-DM is kind of iterative development, we have adopted it with agile methodology, which is a rapid iterative software development method, in order to reduce the development cycle to be minimal.