An aspect sentiment classification model for graph attention networks incorporating syntactic, semantic, and knowledge

Zhang, Siyu; Gong, Hongfang; She, Lina

doi:10.1016/j.knosys.2023.110662

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…Li, Li, and Xiao (2023) proposed an aspect supervision contrastive learning (APSCL) model, to capture the potential relationship between multiple aspects in the emotion subspace. To alleviate the complexity and insensitivity of syntactic relations, Zhang et al (2023) proposed the SSK‐GAT model that integrates syntax, semantics and knowledge. A GMF‐SKIA model based on gated mechanism with fused emotion knowledge and aspect interdependence was proposed by Han et al (2023) And this model is able to dynamically fuse the emotion knowledge information and aspect interdependence of words.…”

Section: Related Workmentioning

confidence: 99%

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Zhou,

Hu,

Liu

et al. 2024

Expert Systems

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The negative herding effect tends to cause the spread of negative emotions among netizens, resulting in significant public opinion crises. Therefore, linking the negative herding effect and online public opinion, we aim to exploring the connection between netizens' emotions, opinions, and the negative herding effect. To better distinguish the emotions in texts, a new BiGADG (Bidirectional Gated recurrent unit based on Attention with Distance‐based dependency syntax Graph convolutional network) model is proposed in this article. This model includes primarily the proposed dependency syntax based on dependency distance, Bidirectional Gated Recurrent Unit (BiGRU), multi‐head self‐attention and Graph Convolutional Network (GCN). Particularly, the dependency syntax based on dependency distances incorporates word‐to‐word dependency distances into the traditional dependency syntax, which enriches the syntactic structural features of the text. After the experiments, the accuracy of BiGADG model is 86.53%, which is 3.11% higher compared to BERT model. It is evident that our model for sentiment classification has been improved. Based on the results of sentiment classification, we investigate the existence of negative herding behaviour and its underlying motivation in online public opinion. We have found that the negative herding effect, if not effectively managed, may trigger large‐scale and uncontrollable public opinion crises.

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Section: Related Workmentioning

confidence: 99%

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Zhou,

Hu,

Liu

et al. 2024

Expert Systems

View full text Add to dashboard Cite

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“…Wang et al reconstructed the syntax dependency tree by pruning the irrelevant edges and encode the revised dependency tree using GAT [16]. Considering the interaction between syntactic and semantic information, multi-channel GCN-based approaches has been proposed to extract syntactic and semantic information [18,19,23]. Zhang et al [20] generated an attention matrix and a syntactic mask matrix based on word-syntactic distances, which are processed to enhance the interaction between semantics and syntax.…”

Section: Aspect-level Sentiment Analysismentioning

confidence: 99%

“…• DR-BERT [43]: A dynamic re-weighting BERT model is built, which tends to learn the dynamic aspect-oriented semantic information. • SSK-GAT+BERT [23]: A novel graph attention network model is proposed to incorporate syntactic, semantic, and knowledge-based features. The model is trained for 15 epochs with a batch size of 16 and a learning rate of 0.000022.…”

mentioning

confidence: 99%

Syntactic Structure-Enhanced Dual Graph Convolutional Network for Aspect-Level Sentiment Classification

Chen,

Qiu,

Liu

et al. 2023

Mathematics

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Aspect-level sentiment classification (ALSC) is a fine-grained sentiment analysis task that aims to predict the sentiment of the given aspect in a sentence. Recent studies mainly focus on using the Graph Convolutional Networks (GCN) to deal with both the semantics and the syntax of a sentence. However, the improvement is limited since the syntax dependency trees are not aspect-oriented and the exploitation of syntax structure information is inadequate. In this paper, we propose a Syntactic Structure-Enhanced Dual Graph Convolutional Network (SSEDGCN) model for an ALSC task. Firstly, to enhance the relation between aspect and its opinion words, we propose an aspect-wise dependency tree by reconstructing the basic syntax dependency tree. Then, we propose a syntax-aware GCN to encode the new tree. For semantics information learning, a semantic-aware GCN is established. In order to exploit syntactic structure information, we design a syntax-guided contrastive learning objective that makes the model aware of syntactic structure and improves the quality of the feature representation of the aspect. The experimental results on three benchmark datasets show that our model significantly outperforms the baseline models and verifies the effectiveness of our model.

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“…On this basis, Yang et al [23] proposed a full graph attention neural network (FGANN), which also considers the influence of nodes other than neighboring nodes and can handle the graph classification task well. Zhang et al [24] noticed that in sentiment classification tasks, traditional models are not sensitive to syntactic structural information due to the complexity of syntactic analysis relationships, and the models lack the utilization of external sentiment knowledge. In this regard, a new graph attention neural network is proposed that realizes attention to multiple levels of syntax, semantics, and knowledge by introducing the mechanism of multi-head attention and acquires knowledge about sentiment by considering information such as syntax.…”

Section: Introductionmentioning

confidence: 99%

Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion

Ai,

Cao,

Wang

et al. 2023

Applied Sciences

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At present, there are problems such as low fault data, insufficient labeling information, and poor fault diagnosis in the field of ship engine diagnosis. To address the above problems, this paper proposes a fault diagnosis method based on probabilistic similarity and rank-order similarity of multi-head graph attention neural networks (MPGANN) models. Firstly, the ship engine dataset is used to explore the similarity between the data using the probabilistic similarity of T_SNE and the rank order similarity of Spearman’s correlation coefficient to define the neighbor relationship between the samples, and then the appropriate weights are selected for the early fusion of the two graph structures to fuse the feature information of the two scales. Finally, the graph attention neural networks (GANN) incorporating the multi-head attention mechanism are utilized to complete the fault diagnosis. In this paper, comparative experiments such as graph construction and algorithm performance are carried out based on the simulated ship engine dataset, and the experimental results show that the MPGANN outperforms the comparative methods in terms of accuracy, F1 score, and total elapsed time, with an accuracy rate of 97.58%. The experimental results show that the model proposed in this paper can still fulfill the ship engine fault diagnosis task well under unfavorable conditions such as small samples and insufficient label information, which is of practical significance in the field of intelligent ship cabins and fault diagnosis.

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An aspect sentiment classification model for graph attention networks incorporating syntactic, semantic, and knowledge

Cited by 10 publications

References 26 publications

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Syntactic Structure-Enhanced Dual Graph Convolutional Network for Aspect-Level Sentiment Classification

Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion

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