Amazing technological breakthrough possible @S-Logix

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • +91- 81240 01111

Social List

Deep learning for affective computing: Text-based emotion recognition in decision support - 2018

Deep Learning For Affective Computing: Text-Based Emotion Recognition In Decision Support

Research Paper on Deep Learning For Affective Computing: Text-Based Emotion Recognition In Decision Support

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.

Deep Learning
Affective Computing
Emotion Recognition
Decision Support

Author(s) Name:  Bernhard Kratzwald,SuzanaIli,MathiasKraus,Stefan Feuerriegel and Helmut Prendinger

Journal name:  Decision Support Systems

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.dss.2018.09.002

Volume Information:  Volume 115, November 2018, Pages 24-35