Actor level emotion magnitude prediction in text and speech
Document Type
Article
Publication Date
1-1-2013
Abstract
The digital universe is expanding at very high rates. New ways of retrieving and enriching text and audio content are required. In this work, a methodology for actor level emotion magnitude prediction in text and speech is proposed. A model is trained to predict emotion magnitudes per actor at any point in a story using previous emotion magnitudes plus current text and speech features which act on the actor's emotional state. The methodology compares linear and non-linear regression techniques to determine the optimal model that fits the data. Results of the analysis show that non-linear regression models based on Support Vector Regression (SVR) using a Radial Basis Function (RBF) kernel provide the most accurate prediction model. An analysis of the contribution of the features for emotion magnitude prediction is performed. © Springer Science+Business Media, LLC 2011.
Publication Source (Journal or Book title)
Multimedia Tools and Applications
First Page
319
Last Page
332
Recommended Citation
Calix, R., & Knapp, G. (2013). Actor level emotion magnitude prediction in text and speech. Multimedia Tools and Applications, 63 (3), 319-332. https://doi.org/10.1007/s11042-011-0909-8