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.

中文翻译：

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基于深度学习的情感计算

目的

人类的决策受到情绪和情绪的影响。情感计算考虑到了这一点，旨在根据人们的情绪状态定制决策支持。然而，情绪的表示和分类是一项非常具有挑战性的任务。该研究使用深度学习模型的定制方法来帮助准确分类情绪和情绪。

设计/方法/方法

本研究提出了使用文本和图像数据的情感计算模型。使用三个深度学习定制模型，即 LSTM、GRU 和 CNN，在四个标准数据集上进行基于文本的情感计算。该研究使用了深度学习的四种变体，包括 LSTM 模型、具有 GloVe 嵌入的 LSTM 模型、双向 LSTM 模型和具有注意力层的 LSTM 模型。

发现

结果表明，所提出的方法优于早期的方法。对于基于图像的情感计算，从 Instagram 中提取数据，并使用三种深度学习模型进行面部情感识别，即 CNN、使用 VGG-19 模型的迁移学习和使用 ResNet-18 模型的迁移学习。结果表明，所提出的文本和图像方法均可用于情感计算和辅助决策。

原创性/价值

该研究将深度学习用于情感计算。早期的研究使用机器学习算法进行情感计算。然而，本研究使用深度学习进行情感计算。