Aspect sentiment analysis with heterogeneous graph neural networks

Lu, Guangquan; Li, Jiecheng; Jian, Wei

doi:10.1016/j.ipm.2022.102953

Cited by 46 publications

(7 citation statements)

References 11 publications

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“…Liang et al [11] used constituent tree to build adjacent matrix for each phrase layer. Hete_GNNs [39] and Sentic GCN [19] introduced sentiment dictionary into words relationship. In addition, BERTbased context representation models [40]- [42] can improve the performance whatever it is a graph-based model or none graph-based model [43].…”

Section: Related Workmentioning

confidence: 99%

Syntactic and Semantic Aware Graph Convolutional Network for Aspect-Based Sentiment Analysis

Chen,

Fan,

Wang

2024

IEEE Access

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In recent years, there has been a growing interest in utilizing dependency parsing with graph convolutional networks for aspect-based sentiment analysis. Dependency relations between words are used to construct graphs that integrate syntactic information into deep learning frameworks. However, most existing methods fail to consider the impact of different relation types between content words, which makes it difficult to distinguish important related words. Moreover, the semantic relationship between words can enhance the text understanding ability, which has been largely neglected in previous works. To address these limitations, in this paper, we propose a novel model named as SS-GCN. Our model automatically learns syntactic weighted matrix and leverages semantic information to obtain the text semantic representation, and an attention module is introduced to obtain the specific aspect-context hidden vectors. The model enhances the text representation ability from syntactic and semantic graph convolutional networks. We conducted comprehensive experiments on publicly available datasets to demonstrate its validity and effectiveness. The experimental results demonstrate that our model outperforms strong baseline models.

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Section: Related Workmentioning

confidence: 99%

Syntactic and Semantic Aware Graph Convolutional Network for Aspect-Based Sentiment Analysis

Chen,

Fan,

Wang

2024

IEEE Access

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“…Heterogeneous global graph neural networks were utilized by Pang et al [32] to consider item transition patterns from other users' historical sessions for deducing user preferences from the current and historical sessions. Lu et al [33] applied heterogeneous graph attention networks for aspect sentiment analysis. Liang et al [34] designed a heterogeneous graph-based model for emotional conversation generation.…”

Section: Heterogeneous Graph Attention Network (Hgat)-related Workmentioning

confidence: 99%

Joint Extraction of Entities and Relations via Entity and Relation Heterogeneous Graph Attention Networks

Jiang

Cao

2023

Applied Sciences

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Entity and relation extraction (ERE) is a core task in information extraction. This task has always faced the overlap problem. It was found that heterogeneous graph attention networks could enhance semantic analysis and fusion between entities and relations to improve the ERE performance in our previous work. In this paper, an entity and relation heterogeneous graph attention network (ERHGA) is proposed for joint ERE. A heterogeneous graph attention network with a gate mechanism was constructed containing word nodes, subject nodes, and relation nodes to learn and enhance the embedding of parts for relational triple extraction. The ERHGA was evaluated on the public relation extraction dataset named WebNLG. The experimental results demonstrate that the ERHGA, by taking subjects and relations as a priori information, can effectively handle the relational triple extraction problem and outperform all baselines to 93.3%, especially overlapping relational triples.

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“…For a single category, let TP be the number of correctly predicted samples, FP be the number of samples predicted to be the current category from other categories, and FN be the number of samples predicted to be other categories from the current category. Then, the calculation formulas for accuracy and MF1 are distributed as follows in (18) and (19).…”

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

“…In recent years, deep learning methods represented by neural networks have attracted increasing attention because they can automatically generate useful feature representations from aspects and their contexts, and can achieve better aspect-level sentiment classification without handcrafted features. In particular, the attention mechanism [9,10] and graph neural network [11][12][13][14][15] methods are widely used in aspect-level sentiment classification due to their ability to focus on aspect words in sentences and their ability to handle unstructured data [16][17][18][19][20][21][22][23]. For example, Su et al [16] proposed a progressive self-supervision attention learning approach for attentional aspect-level sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

Dual-Channel Interactive Graph Convolutional Networks for Aspect-Level Sentiment Analysis

Lan

Yang

2022

Mathematics

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Aspect-level sentiment analysis aims to identify the sentiment polarity of one or more aspect terms in a sentence. At present, many researchers have applied dependency trees and graph neural networks (GNNs) to aspect-level sentiment analysis and achieved promising results. However, when a sentence contains multiple aspects, most methods model each aspect independently, ignoring the issue of sentiment connection between aspects. To address this problem, this paper proposes a dual-channel interactive graph convolutional network (DC-GCN) model for aspect-level sentiment analysis. The model considers both syntactic structure information and multi-aspect sentiment dependencies in sentences and employs graph convolutional networks (GCN) to learn its node information representation. Particularly, to better capture the representations of aspect and opinion words, we exploit the attention mechanism to interactively learn the syntactic information features and multi-aspect sentiment dependency features produced by the GCN. In addition, we construct the word embedding layer by the BERT pre-training model to better learn the contextual semantic information of sentences. The experimental results on the restaurant, laptop, and twitter datasets show that, compared with the state-of-the-art model, the accuracy is up to 1.86%, 2.50, 1.36%, and 0.38 and the Macro-F1 values are up to 1.93%, 0.61%, and 0.4%, respectively.

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Aspect sentiment analysis with heterogeneous graph neural networks

Cited by 46 publications

References 11 publications

Syntactic and Semantic Aware Graph Convolutional Network for Aspect-Based Sentiment Analysis

Syntactic and Semantic Aware Graph Convolutional Network for Aspect-Based Sentiment Analysis

Joint Extraction of Entities and Relations via Entity and Relation Heterogeneous Graph Attention Networks

Dual-Channel Interactive Graph Convolutional Networks for Aspect-Level Sentiment Analysis

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