Ontology-Based Enneagram Personality Prediction System

Abstract

Abstract Researchers are keen on finding out about people’s emotions and interests. Personality prediction helps in this issue. Recognizing consumers’ sentiments and desires assists in the development of better recommendation systems and dating applications. Previous personality prediction systems studies had shown personality theories such as Big Five Traits, Three Factor Model, etc. More informative personality model is required because it offers a greater understanding. The target is enabling machines to understand the person more deeply than the previously used models. Enneagram is a distinct personality theory which demonstrates personalities’ motivations, desires and fears. The questionnaire-based exam is the way to inform a person’s Enneagram personality. People are not motivated to complete the exam because it takes time. Enneagram personality prediction system is presented utilizing Enneagram personality model and Twitter text. This does not require any time or effort to predict the personality of the Enneagram. Personality prediction of the Enneagram applies ontology, lexicon and a statistical method. The system’s performance is evaluated using precision, recall, f1-score, and accuracy. The highest personality type recall output is the Enthusiast which is 95%. This is the first technique to predict Enneagram personality from text. The result indicates a good start to predict Enneagram personality.

Keywords

• Text mining
• Personality prediction
• Intelligent systems
• Personality model
• Enneagram
• Psychology