This dissertation describes an interdisciplinary study that considers linguistic and psychological findings to perform computer-aided categorization of opinions and emotions in texts. It discusses various emotional corpora (movie reviews, weblogs, product reviews, and natural-language dialogues) and describes different approaches to affect classification of their texts: a statistical approach that utilizes lexical, deictic, stylometric, and grammatical information; a semantic approach that relies on emotional dictionaries and on deep grammatical analysis; a hybrid approach that combines the statistical approach and the semantic approach. Furthermore, this thesis explores affect sensing using multimodal fusion that utilizes lexical and acoustic data. In conclusion, the thesis discusses significant contributions and describes future work.

Alexander Osherenko graduated from University of Applied Sciences, Hamburg with the B.Sc. degree in computer science. He obtained his M. Sc. degree in computer science from Humboldt University, Berlin and the PhD degree from University of Augsburg, Germany.