Machine Learning based approach for Human Trait Identification from Blog Data

Emotions form a major part of a person's personality. Emotional intelligence (EI) is the ability to identify, assess, and control the emotions of oneself, of others, and of groups. The written expressions reflect author's personality. Various personality traits can be determined by the analysis of the contents written by a person. This paper proposes a novel technique for human trait identification from the analysis of author's written expressions. The proposed technique is based on the concept of supervised machine learning and uses Support Vector Machine for classifying the personality of a writer. We classify the personality of a writer into five categories namely, highly extrovert, highly introvert, low introvert, low extrovert and ambivert. Experiments have been carried out on the real world blog data and results demonstrate that the proposed technique can determine the personality traits of a writer with accuracy and speed. We have also implemented a PHP based online system, which reads the contents of a blog and can automatically predict the personality of writer of the blog