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.
