An Interactive Graph Attention Networks Model for Aspect-level Sentiment Analysis

Hu HAN; Yuanhang WU; Xiaoya QIN

doi:10.11999/JEIT210036

Volume 43 Issue 11

Nov. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(11): 3282-3290

Hu HAN, Yuanhang WU, Xiaoya QIN. An Interactive Graph Attention Networks Model for Aspect-level Sentiment Analysis[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3282-3290. doi: 10.11999/JEIT210036

Citation:

Hu HAN, Yuanhang WU, Xiaoya QIN. An Interactive Graph Attention Networks Model for Aspect-level Sentiment Analysis[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3282-3290. doi: 10.11999/JEIT210036

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An Interactive Graph Attention Networks Model for Aspect-level Sentiment Analysis

doi: 10.11999/JEIT210036

Hu HAN^{1, 2},
Yuanhang WU^{1
,
,},
Xiaoya QIN¹

1.
School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2.
Gansu Artificial Intelligence and Graphic Image Engineering Research Center, Lanzhou 730070, China

Funds: The National Natural Science Foundation of China (62166024), The National Social Science Foundation of China (17BXW071)

Received Date: 2021-01-11
Rev Recd Date: 2021-09-27

Available Online: 2021-10-09

Publish Date: 2021-11-23

Abstract

Abstract

At present, aspect-level sentiment analysis uses mainly the method of combining attention mechanism and traditional neural network to model aspect and contextual words.These methods ignore the syntactic dependency information and position information between aspects and contextual words in sentences, which leads to unreasonable weight allocation of attention. Therefore, an Interactive Graph ATtention (IGATs) networks model for aspect-level sentiment analysis is proposed. Bidirectional Long Short-Term Memory (BiLSTM) network is firstly used to learn the semantic feature representation of sentences. And then the position information is combined to update the feature representation, a graph attention network is constructed on the newly generated feature representation to capture syntactic dependency information. Finally, interactive attention mechanism is used to model the semantic relations between the aspect and contextual words. Experimental results on three public datasets show that the accuracy and macro average F1 value of IGATs are significantly improved compared with other existing models.
- Natural language processing,
- Aspect-level sentiment analysis,
- Position embedding,
- Graph attention network,
- Attention mechanism

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References(20)

References

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