Emotion Classification in a Serious Game for Training Commu- nication Skills

We describe the natural language processing component of a new serious gaming project, deLearyous, which aims at developing an environment in which users can improve their communication skills by interacting with a virtual character in (Dutch) written natural language. The virtual characters’ possible dialogue paths are defined by Leary’s Rose, a framework for interpersonal communication. In order to apply this framework, input sentences must be classified into one of four possible “emotion” classes. We tried to carry out this emotion classification task using several machine learning algorithms. More specifically, classification performance was measured using TiMBL –a memory-based learner–, a Naı̈ve Bayes classifier, Support Vector Machines and Conditional Random Fields. Training was done on a relatively small dataset of manually tagged sentences. A large number of different features was extracted from the dataset, and a good feature subset was selected using a combination of a genetic algorithm and various filter