Year of Award

2012

Document Type

Thesis

Degree Type

Master of Science (MS)

Degree Name

Computer Science

Department or School/College

Department of Computer Science

Committee Chair

Douglas Raiford

Commitee Members

Allen D. Szalda-Petree, Min Chen

Keywords

emotion, machine learning, random forest, streaming

Abstract

The ability for a computer to recognize emotions would have many uses. In the field of human-computer interaction, it would be useful if computers could sense if a user is frustrated and offer help (Lisetti & Nasoz, 2002), or it could be used in cars to predict stress or road rage (Nasoz, Lisetti, & Vasilakos, 2010). Also, it has uses in the medical field with emotional therapy or monitoring patients (Rebenitsch, Owen, Brohil, Biocca, & Ferydiansyah, 2010). Emotion recognition is a complex subject that combines psychology and computer science, but it is not a new problem. When the question was first posed, researchers examined at physiological signals that could help differentiate an emotion (Schachter & Singer, 1962). As the research progressed, researchers examined ways in which computers could recognize emotions, many of which were successful. Previous research has not yet looked at the emotional data as streaming data, or attempted to classify emotion in real time. This thesis extracts features from a window of simulated streaming data to attempt to classify emotions in real time. As a corollary, this method can also be used to attempt to identify the earliest point an emotion can be predicted. The results show that emotions can be classified in real time, and applying a window and feature extraction leads to better classification success. It shows that this method may be used to determine if an emotion could be predicted before it is cognitively experienced, but it could not predict the emotion transitional state. More research is required before that goal can be achieved.

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© Copyright 2012 Nathan J. Elmore