Aspect-Context Interactive Attention Representation for Aspect-Level Sentiment Classification

Wu, Zhuojia; Li, Yang; Jian, Lin; Li, Deyu; Liu, Yuanyuan; Wang, Suge

doi:10.1109/access.2020.2972697

Cited by 15 publications

(12 citation statements)

References 37 publications

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“…• MultiACIA: To generate intersequence representations in contexts and aspects, this interactive aspect contextual representation system relies solely on the attention mechanism [48].…”

Section: Model Comparisonmentioning

confidence: 99%

Aspect-Level Sentiment Analysis Based on Bidirectional-GRU in SIoT

et al. 2021

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A variety of independent research activities have recently been undertaken to explore the feasibility of incorporating social networking principles into the Internet of Things solutions. The resulting model, called the Social Internet of Things, has the potential to be more powerful and competitive in supporting new IoT applications and networking services. This paper's main contribution is in sentiment analysis, which aims to predict aspect sentiments to improve the making of automated decisions and communication between associates in the social internet of things. In recent years, to analyze sentiment polarity at a subtle level, sentiment classification has become a primetime attraction. Current approaches commonly use the Long-Short Term Memory network to figure aspects and contexts separately. Usually, they perform sentiment classification using simple attention mechanisms and avoiding the bilateral information between sentences and their corresponding aspects. Therefore, the results are not satisfactory. This manuscript intends to develop a new Bidirectional gated recurrent unit model by depending on natural language processing for fully-featured mining to perform the aspect-level sentiment classification task. Our proposed model uses the Bidirectional gated recurrent unit network to acquire the dependency-based semantic analysis of sentences and their corresponding terms compared to earlier work. At the same time, it proposes a method to learn the sentiment polarity of terms in sentences. To check out our model's achievements, we perform several experiments on datasets, namely, (LAPTOP, RESTUARANT, and TWITTER). Our experiment results demonstrate that our model has achieved compelling performance and efficiency improvements in aspect sentiment classification compared with several existing models.

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“…• MultiACIA: To generate intersequence representations in contexts and aspects, this interactive aspect contextual representation system relies solely on the attention mechanism [48].…”

Section: Model Comparisonmentioning

confidence: 99%

Aspect-Level Sentiment Analysis Based on Bidirectional-GRU in SIoT

et al. 2021

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“…Subsequently, the structure named Attention over Attention by Cui et al, an improved version of the attention mechanism, was first applied in the field of cloze-style reading comprehension and achieved excellent results [13]. Then there are many models that borrow AOA to implement aspect-level sentiment analysis tasks [14], [15]. This structure can calculate the attention of both the query and the document at the same time, and can benefit from the mutual information.…”

Section: B Sentiment Classification At Aspect-level With Neural Networkmentioning

confidence: 99%

“…In addition, Attention over Attention (AOA) solves the problem that the above method only considers the attention from aspect to text. It can simultaneously generate attention from aspect to text and text to aspect [13]- [15]. It is true that aspect phrases often have multiple words, but their models are too focused on solving the problem of the correlation between context and aspect.…”

Section: Introductionmentioning

confidence: 99%

Multi-Grained Attention Representation With ALBERT for Aspect-Level Sentiment Classification

et al. 2021

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Aspect-level sentiment classification aims to solve the problem, which is to judge the sentiment tendency of each aspect in a sentence with multiple aspects. Previous works mainly employed Long Short-Term Memory (LSTM) and Attention mechanisms to fuse information between aspects and sentences, or to improve large language models such as BERT to adapt aspect-level sentiment classification tasks. The former methods either did not integrate the interactive information of related aspects and sentences, or ignored the feature extraction of sentences. This paper proposes a novel multi-grained attention representation with ALBERT (MGAR-ALBERT). It can learn the representation that contains the relevant information of the sentence and the aspect, while integrating it into the process of sentence modeling with multi granularity, and finally get a comprehensive sentence representation. In Masked LM (MLM) task, in order to avoid the influence of aspect words being masked in the initial stage of the pre-training, the noise linear cosine decay is introduced into n − gram. We implemented a series of comparative experiments to verify the effectiveness of the method. The experimental results show that our model can achieve excellent results on Restaurant dataset with numerous number of parameters reduced, and it is not inferior to other models on Laptop dataset.INDEX TERMS Aspect-level sentiment classification, ALBERT, natural language processing, deep learning.

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“…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%

“…For example, Su et al [16] proposed a progressive self-supervision attention learning approach for attentional aspect-level sentiment analysis. Wu et al [17] adopted a multi-head attention mechanism to generate aspect and context feature representations. Zhang et al [22] used graph convolutional networks to learn node information in dependency trees and combined attention mechanisms for sentiment classification tasks.…”

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-Context Interactive Attention Representation for Aspect-Level Sentiment Classification

Cited by 15 publications

References 37 publications

Aspect-Level Sentiment Analysis Based on Bidirectional-GRU in SIoT

Aspect-Level Sentiment Analysis Based on Bidirectional-GRU in SIoT

Multi-Grained Attention Representation With ALBERT for Aspect-Level Sentiment Classification

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

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