PETEEI: a PET with evolving emotional intelligence
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Statistics and Computing
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Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Emotions from text: machine learning for text-based emotion prediction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Emotion classification using massive examples extracted from the web
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Natural Language Processing with Python
Natural Language Processing with Python
Affect corpus 2.0: an extension of a corpus for actor level emotion magnitude detection
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IEEE Transactions on Audio, Speech, and Language Processing
Feature Analysis and Evaluation for Automatic Emotion Identification in Speech
IEEE Transactions on Multimedia
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IEEE Transactions on Multimedia
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Multimedia Tools and Applications
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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.