Detecting Players Personality Behavior with Any Effort of Concealment

We introduce a novel natural language processing component using machine learning techniques for prediction of personality behaviors of players in a serious game, Land Science, where players act as interns in an urban planning firm and discuss in groups their ideas about urban planning and environmental science in written natural language. Our model learns vector space representations for various features extraction. In order to apply this framework, input excerpts must be classified into one of six possible personality classes. We applied this personality classification task using several machine learning algorithms, such as: Naive Bayes, Support Vector Machines, and Decision Tree. Training is performed on a relatively dataset of manually annotated excerpts. By combining these features spaces from psychology and computational linguistics, we perform and evaluate our approaches to detecting personality, and eventually develop a classifier that is nearly 83% accurate on our dataset. Based on the feature analysis of our models, we add several theoretical contributions, including revealing a relationship between different personality behaviors in players' writing.