Linguistic features based personality recognition using social media data

• Published in: 2017 6th National Conference on Technology and Management (NCTM) pp. 63 - 68
• Main Authors: Sewwandi, Dilini, Perera, Kusal, Sandaruwan, Sajith, Lakchani, Oshani, Nugaliyadde, Anupiya, Thelijjagoda, Samantha
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2017
• Subjects: Decision support systems; Eysenck's Three Factor model; LIWC (Linguistic Inquiry and Word Count) features; Machine Learning Algorithms; Ontology; Semantic Analysis
• DOI: 10.1109/NCTM.2017.7872829

Social media has become a prominent platform for opinions and thoughts. This stated that the characteristics of a person can be assessed through social media status updates. The purpose of this research article is to provide a web application in order to detect one's personality using linguistic feature analysis. The personality of a person has classified according to Eysenck's Three Factor personality model. The proposed technique is based on ontology based text classification, linguistic feature-vector matrix using LIWC (Linguistic Inquiry and Word Count) features including semantic analysis using supervised machine learning algorithms and questionnaire based personality detection. This is vital for HR management system when recruiting and promoting employees, R&D Psychologists can use the dynamic ontology for storage purposes and all the other API users including universities and sports clubs. According to the test results the proposed system is in an accuracy level of 91%, when tested with a real world personality detection questionnaire based application, and results demonstrate that the proposed technique can detect the personality of a person with considerable accuracy and a speed.