Deep learning based affective computing
Journal of Enterprise Information Management
ISSN: 1741-0398
Article publication date: 18 October 2021
Issue publication date: 9 November 2021
Abstract
Purpose
Decision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments.
Design/methodology/approach
The present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer.
Findings
The result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making.
Originality/value
The study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.
Keywords
Citation
Kumar, S. (2021), "Deep learning based affective computing", Journal of Enterprise Information Management, Vol. 34 No. 5, pp. 1551-1575. https://doi.org/10.1108/JEIM-12-2020-0536
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited