Personality Classification from Online Text using Machine Learning Approach

• Published in: International journal of advanced computer science & applications Vol. 11; no. 3
• Main Authors: Khan, Alam Sher, Ahmad, Hussain, Zubair, Muhammad, Khan, Furqan, Arif, Areeba, Ali, Hassan
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2020
• Subjects: Classifiers; Datasets; Extroversion; Introversion; Machine learning; Personality; Personality traits; Sampling methods
• DOI: 10.14569/IJACSA.2020.0110358
• ISSN: 2158-107X

Personality refer to the distinctive set of characteristics of a person that effect their habits, behaviour’s, attitude and pattern of thoughts. Text available on Social Networking sites provide an opportunity to recognize individual’s personality traits automatically. In this proposed work, Machine Learning Technique, XGBoost classifier is used to predict four personality traits based on Myers- Briggs Type Indicator (MBTI) model, namely Introversion-Extroversion(I-E), iNtuition-Sensing(N-S), Feeling-Thinking(F-T) and Judging-Perceiving(J-P) from input text. Publically available benchmark dataset from Kaggle is used in experiments. The skewness of the dataset is the main issue associated with the prior work, which is minimized by applying Re-sampling technique namely random over-sampling, resulting in better performance. For more exploration of the personality from text, pre-processing techniques including tokenization, word stemming, stop words elimination and feature selection using TF IDF are also exploited. This work provides the basis for developing a personality identification system which could assist organization for recruiting and selecting appropriate personnel and to improve their business by knowing the personality and preferences of their customers. The results obtained by all classifiers across all personality traits is good enough, however, the performance of XGBoost classifier is outstanding by achieving more than 99% precision and accuracy for different traits.