Affect-GCN: a multimodal graph convolutional network for multi-emotion with intensity recognition and sentiment analysis in dialogues

Firdaus, Mauajama; Singh, Gopendra Vikram; Ekbal, Asif; Bhattacharyya, Pushpak

doi:10.1007/s11042-023-14885-1

Cited by 7 publications

(2 citation statements)

References 49 publications

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“…Similarly, GCNs also have many applications in the field of natural language processing. In terms of sentiment analysis, they are not only applicable to unimodal sentiment analysis ( Zhang et al, 2022 ) but also to multimodal sentiment analysis ( Firdaus et al, 2023 ). For example, Huang et al (2023) propose CRF-GCN, a model that utilizes conditional random fields (CRF) to extract opinion scopes of specific aspect words and integrates their contextual information into global nodes.…”

Section: Related Wordsmentioning

confidence: 99%

“…Similarly, GCNs also have many applications in the field of natural language processing. In terms of sentiment analysis, they are not only applicable to unimodal sentiment analysis (Zhang et al, 2022) but also to multimodal sentiment analysis (Firdaus et al, 2023). For example, Huang et al (2023) propose CRF-GCN, a model that utilizes conditional random fields (CRF) to extract opinion scopes of specific aspect words and (Bruna et al, 2014) in 2014, which imitates the characteristics of convolutional neural networks by superimposing multi-layer graph convolutions, and defines convolutional kernels and activation functions for each layer, and form graph convolutional neural networks.…”

Section: Related Wordsmentioning

confidence: 99%

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Dual-channel deep graph convolutional neural networks

Ye,

Li,

et al. 2024

Front. Artif. Intell.

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The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks. However, current dual-channel graph convolutional neural networks are limited by the number of convolution layers, which hinders the performance improvement of the models. Graph convolutional neural networks superimpose multi-layer graph convolution operations, which would occur in smoothing phenomena, resulting in performance decreasing as the increasing number of graph convolutional layers. Inspired by the success of residual connections on convolutional neural networks, this paper applies residual connections to dual-channel graph convolutional neural networks, and increases the depth of dual-channel graph convolutional neural networks. Thus, a dual-channel deep graph convolutional neural network (D2GCN) is proposed, which can effectively avoid over-smoothing and improve model performance. D2GCN is verified on CiteSeer, DBLP, and SDBLP datasets, the results show that D2GCN performs better than the comparison algorithms used in node classification tasks.

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Section: Related Wordsmentioning

confidence: 99%

“…Similarly, GCNs also have many applications in the field of natural language processing. In terms of sentiment analysis, they are not only applicable to unimodal sentiment analysis (Zhang et al, 2022) but also to multimodal sentiment analysis (Firdaus et al, 2023). For example, Huang et al (2023) propose CRF-GCN, a model that utilizes conditional random fields (CRF) to extract opinion scopes of specific aspect words and (Bruna et al, 2014) in 2014, which imitates the characteristics of convolutional neural networks by superimposing multi-layer graph convolutions, and defines convolutional kernels and activation functions for each layer, and form graph convolutional neural networks.…”

Section: Related Wordsmentioning

confidence: 99%