research-article

SC-DGCN: Sentiment Classification Based on Densely Connected Graph Convolutional Network

Authors:

Chao ChenAuthors Info & Claims

ICMLC '21: Proceedings of the 2021 13th International Conference on Machine Learning and Computing

Pages 279 - 284

https://doi.org/10.1145/3457682.3457724

Published: 21 June 2021 Publication History

Abstract

Recently, various neural network frameworks have achieved good results in sentiment classification task, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). However, these methods only take into account semantic information in local contexts and ignore the global syntactic structure information due to the network structure. To solve this problem, we propose a novel neural architecture called SC-DGCN that combines Graph Convolutional Network (GCN) and Bi-LSTM. In SC-DGCN model, we utilize a GCN over the dependency tree of a sentence to exploit syntactical information and words dependencies. In addition, we further introduce dense connection strategy into GCN blocks to aggregate more syntactic information from neighbors and multi-hops in the dependency tree, and employ attention mechanism to generate the final representation of text. Our proposed SC-DGCN model can automatically extract semantic feature in local contexts and the global syntactic structure feature. A series of experiments on MR and SST datasets also indicate that our model is effective for sentiment classification task.

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Cited By

Vashisht N(2023)Semantic Representation is Superior to Syntactic Representation for Emotion Classification Using Graph Neural Networks2023 International Conference on Data Science and Network Security (ICDSNS)10.1109/ICDSNS58469.2023.10245002(1-8)Online publication date: 28-Jul-2023
https://doi.org/10.1109/ICDSNS58469.2023.10245002

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ICMLC '21: Proceedings of the 2021 13th International Conference on Machine Learning and Computing

February 2021

601 pages

ISBN:9781450389310

DOI:10.1145/3457682

Copyright © 2021 ACM.

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Published: 21 June 2021

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National Natural Science Foundation of China

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ICMLC 2021

ICMLC 2021: 2021 13th International Conference on Machine Learning and Computing

February 26 - March 1, 2021

Shenzhen, China

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Cited By

Vashisht N(2023)Semantic Representation is Superior to Syntactic Representation for Emotion Classification Using Graph Neural Networks2023 International Conference on Data Science and Network Security (ICDSNS)10.1109/ICDSNS58469.2023.10245002(1-8)Online publication date: 28-Jul-2023
https://doi.org/10.1109/ICDSNS58469.2023.10245002

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