Graph Convolutional Networks with Structural Attention Model for Aspect Based Sentiment Analysis

Chen, Junjie; Hou, Hongxu; Ji, Yatu; Gao, Jing

doi:10.1109/ijcnn.2019.8852093

Cited by 18 publications

(12 citation statements)

References 29 publications

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“… BERT [ 3 ] utilizes the transformer as a submodule and obtains word embeddings by a two-way mechanism. GCNDA [ 23 ] obtains the weight of words by combining the graphed attention mechanism, and it has two attentions, global and local. …”

Section: Experiments and Resultsmentioning

confidence: 99%

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An Improved BERT and Syntactic Dependency Representation Model for Sentiment Analysis

Liu

Zhan-liang

et al. 2022

Computational Intelligence and Neuroscience

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Text representation of social media is an important task for users’ sentiment analysis. Utilizing the better representation, we can accurately acquire the real semantic information expressed by online users. However, existing works cannot achieve the best results. In this paper, we construct and implement a sentiment analysis model based on the improved BERT and syntactic dependency. Firstly, by studying the word embeddings of BERT, we have ameliorated the embeddings representation. Attention mechanism is added to the word embeddings, sentence embeddings, and position embeddings. Secondly, we have exploited the dependency syntax analysis of the text, and the dependency relationship of different syntactic components will be obtained. For different syntactic components, the hierarchical attention mechanism is used to construct the phrase embeddings or block embeddings. Finally, we splice the syntactic blocks for sentiment analysis. Extensive experiments show that the proposed model has a stronger ability than the baselines on two standard data sets.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…GCNDA [ 23 ] obtains the weight of words by combining the graphed attention mechanism, and it has two attentions, global and local.…”

Section: Experiments and Resultsmentioning

confidence: 99%

An Improved BERT and Syntactic Dependency Representation Model for Sentiment Analysis

Liu

Zhan-liang

et al. 2022

Computational Intelligence and Neuroscience

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“…In sentiment classification, a GNN is coming into use in an amount of literature. Chen et al 32 utilized graph convolution neural network and attention mechanism to complete the task of aspect-based sentiment classification. Xiao et al 33 proposed a novel method called AEGCN based on graph attention neural network to accomplish the target sentiment classification task.…”

Section: Gnn Methodsmentioning

confidence: 99%

Implicit sentiment analysis based on graph attention neural network

Yang

Xing

et al. 2021

Engineering Reports

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Sentiment analysis is one of the crucial tasks in the field of natural language processing. Implicit sentiment suffers a significant challenge because the sentence does not include explicit emotional words and emotional expression is vague. This paper proposed a novel implicit sentiment analysis model based on graph attention convolutional neural network. A graph convolutional neural network is used to propagate semantic information. The attention mechanism is employed to compute the contribution to the emotional expression of words.In order to solve the problem of multiple attention preserving repeated information, orthogonal attention constraint was used to make different attention store different emotional information; given the uneven distribution of emotional information, score attention constraint was proposed to make the model focus on a limited number of essential words. The performance of the proposed model was verified on implicit sentiment datasets. The F value reached 88.16%, which is higher than the benchmark model in the literature. The attention mechanism is analyzed to verify the effectiveness of orthogonal constraint and score constraint.

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“…Another popular mainstream approach to recognize emotion is based on graph structure. Graph neural networks (Gori et al, 2005) have also been a popular choice recently and have been applied to sentiment analysis (Chen et al, 2019). For instance, to extend DialogueRNN to consider speaker information of the utterances and the relative positions of other utterances from the target utterance, Ghosal et al (2019) proposes DialogueGCN based on GCN (Schlichtkrull et al, 2018;Defferrard et al, 2016) to leverage these two factors by modelling conversation using a directed graph.…”

Section: Related Workmentioning

confidence: 99%

Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis

Wang¹,

Meng²,

Liu³

et al. 2022

Preprint

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Many studies on dialog emotion analysis focus on utterance-level emotion only. These models hence are not optimized for dialog-level emotion detection, i.e., to predict the emotion category of a dialog as a whole. More importantly, these models cannot benefit from the context provided by the whole dialog. In real-world applications, annotations to dialog could finegrained, including both utterance-level tags (e.g., speaker type, intent category, and emotion category), and dialog-level tags (e.g., user satisfaction, and emotion curve category). In this paper, we propose a Context-based Hierarchical Attention Capsule (Chat-Capsule) model, which models both utterance-level and dialog-level emotions and their interrelations. On a dialog dataset collected from customer support of an e-commerce platform, our model is also able to predict user satisfaction and emotion curve category. Emotion curve refers to the change of emotions along the development of a conversation. Experiments show that the proposed Chat-Capsule outperform stateof-the-art baselines on both benchmark dataset and proprietary dataset. Source code will be released upon acceptance.

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Graph Convolutional Networks with Structural Attention Model for Aspect Based Sentiment Analysis

Cited by 18 publications

References 29 publications

An Improved BERT and Syntactic Dependency Representation Model for Sentiment Analysis

An Improved BERT and Syntactic Dependency Representation Model for Sentiment Analysis

Implicit sentiment analysis based on graph attention neural network

Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis

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