ARCHITECTURE FOR PERSONALITY DETECTION USING ENNEAGRAM KNOWLEDGE: CASE STUDY

Researchers are concerned with automated personality detection from social media. Automated Personality detection from text benefits in social media like: attracting more users, career advising and getting more advertisements. Traditional personality detection is done by using an assessment test. Performing a test is time-consuming so users aren’t interested in taking a test. This paper presents case study and architecture on automated personality from text using twitter text. The case study uses public text to identify the personality of the profile. The applied personality Model is the Enneagram model. Proposed architecture contains four phases: text preprocessing, feature extraction, feature selection and personality detection. Feature selection is done by using Enneagram ontology and lexicon. Personality detection is utilized by using statistical approaches. The Enneagram knowledge is modeled using ontology. The lexicon is a source to enrich the ontology seed. Statistical Approach is utilized to identify the personality. The case study identifies the personality. The highest outcome percentage is “investigator” personality, which is 24 %. This indicates that the personality is an investigator. This result is similar to official Enneagram experts’ analysis. This model is the first one which uses the Enneagram model as automatic detection. Enneagram is a powerful personality model that aids psychiatrists and physicians to understand the patient's personality intensely. This knowledge gives them the tools to support and aid the patient to heal faster. The promising outcomes open the door to further research in this area.