Research Area:  Machine Learning
Emotions widely affect human decision-making. This fact is taken into account by affective computing with the goal of tailoring decision support to the emotional states of individuals. However, the accurate recognition of emotions within narrative documents presents a challenging undertaking due to the complexity and ambiguity of language. Performance improvements can be achieved through deep learning; yet, as demonstrated in this paper, the specific nature of this task requires the customization of recurrent neural networks with regard to bidirectional processing, dropout layers as a means of regularization, and weighted loss functions. In addition, we propose sent2affect, a tailored form of transfer learning for affective computing: here the network is pre-trained for a different task (i.e. sentiment analysis), while the output layer is subsequently tuned to the task of emotion recognition. The resulting performance is evaluated in a holistic setting across 6 benchmark datasets, where we find that both recurrent neural networks and transfer learning consistently outperform traditional machine learning. Altogether, the findings have considerable implications for the use of affective computing.
Author(s) Name:  Bernhard Kratzwald,SuzanaIli,MathiasKraus,Stefan Feuerriegel and Helmut Prendinger
Journal name:  Decision Support Systems
Publisher name:  ELSEVIER
Volume Information:  Volume 115, November 2018, Pages 24-35
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0167923618301519