Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis

Bao, Lingxian; Lambert, Patrik; Badía, Toni

doi:10.18653/v1/p19-2035

Cited by 54 publications

(30 citation statements)

References 16 publications

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“…However, no attention has been paid to aspect information in a sentence. What's more, Bao et al [4] describes an approach of leveraging numerical polarity features provided by existing lexicon resources in an aspect-based sentiment analysis environment with an attention LSTM. It is based on the AT-LSTM model and linearly transforms the emotional word embedding into the final regularization along with the sentence embedding.…”

Section: B Lexicon Enhanced Sentiment Analysismentioning

confidence: 99%

“…As for the sentiment classification, the lexicon is a useful resource. Incorporating sentiment lexicons into neural sentiment classification methods has attracted increasing attention recently [4]- [6]. Since these sentiment words are an important part to convey the sentimental polarity of sentences, and the sentiment lexicons play an important role in sentence sentiment classification.…”

Section: Introductionmentioning

confidence: 99%

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A Lexicon-Enhanced Attention Network for Aspect-Level Sentiment Analysis

et al. 2020

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Aspect-level sentiment classification is a fine-grained task in sentiment analysis. In recent years, researchers have realized the importance of the relationship between aspect term and sentence and many classification models based on deep learning network have been proposed. However, these end-to-end deep neural network models lack flexibility and do not consider the sentiment word information in existing methods. Therefore, we propose a lexicon-enhanced attention network (LEAN) based on bidirectional LSTM. LEAN not only can catch the sentiment words in a sentence but also concentrate on specific aspect information in a sentence. Moreover, leveraging lexicon information will enhance the model's flexibility and robustness. We experiment on the SemEval 2014 dataset and results find that our model achieves state-ofthe-art performance on aspect-level sentiment classification. INDEX TERMS Natural language processing, sentiment analysis, aspect-level, sentiment lexicon, attention mechanism.

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Section: B Lexicon Enhanced Sentiment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Lexicon-Enhanced Attention Network for Aspect-Level Sentiment Analysis

et al. 2020

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“…There are kinds of sentiment analysis tasks, such as documentlevel (Thongtan and Phienthrakul, 2019), sentence-level 4 , aspect-level (Pontiki et al, 2014;Wang et al, 2019a) and multimodal (Chen et al, 2018;Akhtar et al, 2019) sentiment analysis. For the aspect-level sentiment analysis, previous work typically apply attention mechanism (Luong et al, 2015) combining with memory network (Weston et al, 2014) or gating units to solve this task (Tang et al, 2016b;He et al, 2018a;Xue and Li, 2018;Duan et al, 2018;Tang et al, 2019;Yang et al, 2019;Bao et al, 2019), where an aspect-independent encoder is used to generate the sentence representation. In addition, some work leverage the aspect-weakly associative encoder to generate aspect-specific sentence representation (Tang et al, 2016a;Wang et al, 2016;Majumder et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis

Liang¹,

Meng²,

Zhang³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspectindependent (weakly associative) encoder for sentence representation generation. In this paper, we propose a novel Aspect-Guided Deep Transition model, named AGDT, which utilizes the given aspect to guide the sentence encoding from scratch with the speciallydesigned deep transition architecture. Furthermore, an aspect-oriented objective is designed to enforce AGDT to reconstruct the given aspect with the generated sentence representation. In doing so, our AGDT can accurately generate aspect-specific sentence representation, and thus conduct more accurate sentiment predictions. Experimental results on multiple SemEval datasets demonstrate the effectiveness of our proposed approach, which significantly outperforms the best reported results with the same setting 1 .

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“…Later, the neural network method has become popular in this task because of its flexible structure and automatic feature extraction capability. Most neural network models are based on long short-term memory (LSTM) [8]- [11]. To focus on the aspect-specific part of the sentences, many attentionbased models appear [12]- [15].…”

Section: Introductionmentioning

confidence: 99%

Aspect-Specific Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Classification

Hui

Liu

2020

IEEE Access

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Aspect-based sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. There are two main problems with existing methods: First, the methods simply take the average of the sentence and aspect word vectors as the sentence and aspect representations for a certain sentence, but they are not explicit representations and will lose considerable useful information. Second, existing models based on graph convolutional networks (GCNs) only use the dependency relationship of a sentence, which cannot fully exploit the potential of the sentence and exert the powerful feature fusion ability of GCNs. To solve these problems, we propose a novel GCN-based model that uses a heterogeneous graph. We explicitly define sentence and aspect nodes to learn the sentence and aspect representations separately and then combine 4 kinds of relationships to construct the heterogeneous graph. In our experiments conducted on 5 public datasets, the experimental results show that our network consistently outperforms the state-of-the-art model on all these datasets. INDEX TERMS Aspect-based sentiment classification(ABSA), heterogeneous graph, graph convolutional network(GCN).

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Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis

Cited by 54 publications

References 16 publications

A Lexicon-Enhanced Attention Network for Aspect-Level Sentiment Analysis

A Lexicon-Enhanced Attention Network for Aspect-Level Sentiment Analysis

A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis

Aspect-Specific Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Classification

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