Recurrent Attention Network on Memory for Aspect Sentiment Analysis

Chen, Peng; Sun, Zhongqian; Bing, Lidong; Yang, Wei

doi:10.18653/v1/d17-1047

Cited by 836 publications

(505 citation statements)

References 16 publications

(40 reference statements)

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“…The model makes embeddings of aspects to participate in computing attention weights. RAM is proposed by Chen et al [10] which adopts multiple-attention mechanism on the memory built with bidirectional LSTM. Ma et al [11] design a model with the bidirectional attention mechanism, which interactively learns the attention weights on context and aspect words respectively.…”

Section: Aspect-level Sentiment Classificationmentioning

confidence: 99%

“…In recent years, neural network models [5,6] are of growing interest for their capacity to automatically generate useful low dimensional representations from aspects and their contexts, and achieve great accuracy on the aspect-level sentiment classification without careful engineering of features. Especially, by the ability to effectively identify which words in the sentence are more important on a given aspect, attention mechanisms [7,8] implemented by neural networks are widely used in aspect-level sentiment classification [9][10][11][12][13][14]. Chen et al [10] model a multiple attention Email addresses: pinlongzhao@tju.edu.cn (Pinlong Zhao), llhou@mail.nankai.edu.cn (Linlin Hou), wuou@tju.edu.cn (Ou Wu) The setting is romantic, but the food is horrible, the service is pathetic.…”

Section: Introductionmentioning

confidence: 99%

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Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification

Zhao

Hou

2020

Knowledge-Based Systems

152

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Aspect-level sentiment classification aims to distinguish the sentiment polarities over one or more aspect terms in a sentence. Existing approaches mostly model different aspects in one sentence independently, which ignore the sentiment dependencies between different aspects. However, we find such dependency information between different aspects can bring additional valuable information. In this paper, we propose a novel aspect-level sentiment classification model based on graph convolutional networks (GCN) which can effectively capture the sentiment dependencies between multi-aspects in one sentence. Our model firstly introduces bidirectional attention mechanism with position encoding to model aspect-specific representations between each aspect and its context words, then employs GCN over the attention mechanism to capture the sentiment dependencies between different aspects in one sentence. We evaluate the proposed approach on the SemEval 2014 datasets. Experiments show that our model outperforms the state-of-the-art methods. We also conduct experiments to evaluate the effectiveness of GCN module, which indicates that the dependencies between different aspects is highly helpful in aspect-level sentiment classification.

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Section: Aspect-level Sentiment Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification

Zhao

Hou

2020

Knowledge-Based Systems

152

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“…the combination of semantic relatedness and syntactic proximity, but we get unexpected sub-optimal results, which will be shown in experiments section. 3 With spaCy toolkit: https://spacy.io/. 4…”

Section: Position Proximitymentioning

confidence: 99%

Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network

Zhang

Song

2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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It has been widely accepted that Long Short-Term Memory (LSTM) network, coupled with attention mechanism and memory module, is useful for aspect-level sentiment classification. However, existing approaches largely rely on the modelling of semantic relatedness of an aspect with its context words, while to some extent ignore their syntactic dependencies within sentences. Consequently, this may lead to an undesirable result that the aspect attends on contextual words that are descriptive of other aspects. In this paper, we propose a proximity-weighted convolution network to offer an aspect-specific syntax-aware representation of contexts. In particular, two ways of determining proximity weight are explored, namely position proximity and dependency proximity. The representation is primarily abstracted by a bidirectional LSTM architecture and further enhanced by a proximity-weighted convolution. Experiments conducted on the SemEval 2014 benchmark demonstrate the effectiveness of our proposed approach compared with a range of state-of-the-art models 1 .

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“…Traditional machine learning methods for aspectbased sentiment analysis focus on extracting a set of features to train sentiment classifiers (Ding et al, 2009;Boiy and Moens, 2009;Jiang et al, 2011), which usually are labor intensive. With the development of deep learning technologies, neural attention mechanism (Bahdanau et al, 2014) has been widely adopted to address this task (Tang et al, 2015;Wang et al, 2016;Tang et al, 2016;Ma et al, 2017;Chen et al, 2017;Cheng et al, 2017;Li et al, 2018a;Wang et al, 2018a;Tay et al, 2018;Hazarika et al, 2018;Majumder et al, 2018;Fan et al, 2018;Wang et al, 2018b). Wang et al (2016) propose attention-based LSTM networks which attend on different parts of the sentence for different aspects.…”

Section: Related Workmentioning

confidence: 99%

Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis

Hu¹,

Zhao

Guo

et al. 2019

Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

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Aspect-based sentiment analysis (ABSA) is to predict the sentiment polarity towards a particular aspect in a sentence. Recently, this task has been widely addressed by the neural attention mechanism, which computes attention weights to softly select words for generating aspect-specific sentence representations. The attention is expected to concentrate on opinion words for accurate sentiment prediction. However, attention is prone to be distracted by noisy or misleading words, or opinion words from other aspects. In this paper, we propose an alternative hard-selection approach, which determines the start and end positions of the opinion snippet, and selects the words between these two positions for sentiment prediction. Specifically, we learn deep associations between the sentence and aspect, and the long-term dependencies within the sentence by leveraging the pre-trained BERT model. We further detect the opinion snippet by selfcritical reinforcement learning. Especially, experimental results demonstrate the effectiveness of our method and prove that our hardselection approach outperforms soft-selection approaches when handling multi-aspect sentences.

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Recurrent Attention Network on Memory for Aspect Sentiment Analysis

Cited by 836 publications

References 16 publications

Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification

Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification

Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network

Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis

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