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Probabilistic Topic Models for Human Emotion Analysis


Abstract While discrete emotions like joy, anger, disgust etc. are quite popular, continuous

emotion dimensions like arousal and valence are gaining popularity within the research

community due to an increase in the availability of datasets annotated with these

emotions. Unlike the discrete emotions, continuous emotions allow modeling of subtle

and complex affect dimensions but are difficult to predict.

Dimension reduction techniques form the core of emotion recognition systems and

help create a new feature space that is more helpful in predicting emotions. But these

techniques do not necessarily guarantee a better predictive capability as most of them

are unsupervised, especially in regression learning. In emotion recognition literature,

s... (more)
Created Date 2015
Contributor Lade, Prasanth (Author) / Panchanathan, Sethuraman (Advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Balasubramanian, Vineeth N (Committee member) / Arizona State University (Publisher)
Subject Computer science / Arousal and Valence / Change Detection / Dimension Reduction / Emotion Recognition / Regularized Topic Model / Topic Models
Type Doctoral Dissertation
Extent 169 pages
Language English
Note Doctoral Dissertation Computer Science 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis