Multi-interaction Graph Convolutional Networks for Aspect-level Sentiment Analysis

WANG Ruyan; TAO Zhongyuan; ZHAO Rongjian; ZHANG Puning; YANG Zhigang

doi:10.11999/JEIT210459

Volume 44 Issue 3

Mar. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(3): 1111-1118

Youlin GENG, Chengbo XIE, Chuan YIN, Lantu GUO, Xianyi WANG. Received Signal Strength Indication Difference Location Algorithm Based on Kalman Filter[J]. Journal of Electronics & Information Technology, 2019, 41(2): 455-461. doi: 10.11999/JEIT180268

Citation:

WANG Ruyan, TAO Zhongyuan, ZHAO Rongjian, ZHANG Puning, YANG Zhigang. Multi-interaction Graph Convolutional Networks for Aspect-level Sentiment Analysis[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1111-1118. doi: 10.11999/JEIT210459

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Multi-interaction Graph Convolutional Networks for Aspect-level Sentiment Analysis

doi: 10.11999/JEIT210459

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing 400065, China
3.
Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61901071, 61871062, 61771082), The University Innovation Research Group Program of Chongqing (CXQT20017), The Natural Science Foundation of Chongqing (cstc2019jcyj-zdxmX0024)

Received Date: 2021-05-25
Rev Recd Date: 2021-09-07

Available Online: 2021-09-17

Publish Date: 2022-03-28

Abstract

Abstract

Aspect level sentiment analysis aims to identify the sentiment polarity of a specific aspect in a given context, and is a fine-grained sentiment analysis task. The traditional attention-based approach, which only performs the semantic interaction between words, does not establish the syntactic relation interaction between aspect words and text words, resulting in the aspect words incorrectly focusing on information about words that are irrelevant to their syntax. In addition, the positional distance feature and the syntactic distance feature of words, which reflect their relationships in the linear form of the sentence and in the syntactic dependency tree of the sentence, respectively, are ignored by the method of processing syntactic information using graph convolutional networks, allowing irrelevant information far from the aspect words to interfere with their sentiment analysis. To address this problem, a Multi-Interaction Graph Convolutional Network (MIGCN) is proposed. First, the context words positional distance features are fed into each layer of the graph convolutional network, while the adjacency matrix of the graph convolutional network is weighted by using the syntactic distance of context words in the dependency tree. Finally, semantic interaction and syntactic interaction are designed to process the semantic and syntactic information between words, respectively. The experimental results show the proposed model can outperform state-of-the-art baselines on the available datasets.
- Aspect-level sentiment analysis,
- Graph convolutional network,
- Dependency tree,
- Attention mechanisms

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References(19)

References

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