Computing Point-of-View: Modeling and Simulating Judgments of Taste

This dissertation asks whether weblogs, profiles, and email contain enough evidence to model how a particular person judges culture, events, food, humor, and perception. Its sequence is acquire, generalize, and apply: read textual traces for affective themes; extend sparse personal evidence with semantic resources and cultural patterns; then use the model in systems for art, introductions, self-reflection, virtual mentors, family recipes, and humor. Results varied by realm. Taste recommendation scored 0.86 against a 0.73 pairwise baseline; attitude models predicted arousal better than baselines but pleasure and dominance less reliably; one perceptual classification reached 0.62 while another was not significant; and a thirty-six-person mentor study improved learning of explicit attitudes and personality. The dissertation calls these evaluations “modestly successful.” Its five realms overlap, several studies are small, and associative reading can assign surrounding affect to the wrong concept. Advised by Pattie Maes and read by William J. Mitchell and Warren Sack, it is the document that makes the sentiment, semantics, common-sense, and aesthetics work one inquiry.