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Multimedia Tools and Applications

, Volume 62, Issue 2, pp 319–332 | Cite as

Actor level emotion magnitude prediction in text and speech

  • Ricardo A. Calix
  • Gerald M. KnappEmail author
Article

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.

Keywords

Artificial intelligence Natural language processing Machine learning Multimedia semantic analysis Affect detection Speech processing 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.College of EngineeringLouisiana State UniversityBaton RougeUSA

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