Abstract:
Personality refers to the distinguishing set of qualities of an individual that impacts their
attitude, habits, behaviors and pattern of thoughts. Personality traits have been shown
to have governing effect on major outlook of life such as success in the political temperament,
general and workplace emotional stability. Textual data accessible on Social Networking
sites yields an opportunity to automatically identify personality traits of an individual.
Since technology has progressed expeditiously, personality detection has become
a popular research field that bestows personalization to users. Presently, researchers have
employed data on social media for automatic prediction of personality. However, the extraction
of the social media data is a complex process as it is noisy, available in different formats
and lengths. This research proposes a machine learning model and a deep learning model
to predict the personality of an individual based on Myers–Briggs Type Indicator (MBTI)
personality model. The proposed machine learning models (SVM, LR, MLP and XGBoost)
were trained on MBTI and MBTI500 datasets with imbalanced and balanced instances (using
SMOTE). The proposed deep learning model was trained using CNN with GloVe word embeddings.
SVM model achieved the highest accuracy of 96.81% for machine learning model
on MBTI500 dataset with SMOTE. However, CNN exhibited the highest accuracy of 99.54%
on MBTI dataset which supersedes the existing models.
