Using text mining to predict personality based on social behavior

• Main Authors: Chang, Xiao- Zhen, 張曉珍
• Other Authors: Li, Yung-Ming
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/79308521627926064594

碩士 === 國立交通大學 === 管理學院資訊管理學程 === 101 === Nowadays Internet is used for communication widely. People prefer communicating via Internet Services over talking face-to-face or writing letters. They are more often writing blogs or posting messages on social networks and the personality will be presented by habitual vocabularies they used. This research is analyzing Chinese vocabularies to predict personality from posted contents by Facebook users. The personality classification is based on Five Factor Model (Costa &; McCrae, 1985). The five categories are Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness. This research compares two methods. Method one is key vocabulary prediction by using SINICA BOW-WordNet. Method two is machine learning prediction by using compact Bayes theorem. The results show that the accuracy of method two (80%) is better than method one ( 61%). The accuracy of method two will be better when the sample is enough. The result could be used to extend vocabularies of method one and improvements accuracy from 66.67% to 73.33%. This research demonstrates a different way to analyze personality by analyzing posted contents on Facebook from traditional questionnaire and the contribution of this research can provide helpful reference to HR of enterprise when recruiting employees.