Actor level emotion magnitude prediction in text and speech

作者：Calix Ricardo A; Knapp Gerald M^*

来源：Multimedia Tools and Applications, 2013, 62(2): 319-332.

DOI：10.1007/s11042-011-0909-8

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

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%26apos;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 RBF) kernel provide the most accurate prediction model. An analysis of the contribution of the features for emotion magnitude prediction is performed.

• 出版日期2013-1