Deep Selective Memory Network With Selective Attention and Inter-Aspect Modeling for Aspect Level Sentiment Classification

Lin, Peiqin; Yang, Meng; Lai, Jianhuang

doi:10.1109/taslp.2021.3058540

Cited by 17 publications

(4 citation statements)

References 29 publications

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“…Finally a CRF is used to model dependencies among output labels. Lin et al proposed a DSMN [41] to guide multi-hop attention mechanism by computing distance between an aspect and its context to capture aspect aware context information.…”

Section: Related Workmentioning

confidence: 99%

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A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification

Dhanith,

Surendiran,

Rohith

et al. 2024

Neural Process Lett

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Aspect based sentiment analysis (ABSA) manifests the well refined work as Aspect-level sentiment classification (ASC) due to recent high attention and profound outcomes. This paper reveals the exorbitant relation of concerned aspect in determining the sentiment polarity of a sentence in addition to their content. Considering an instance, “The food is tasty but restaurant is untidy”, aspect food reveals the polarity as positive, while in case of restaurant, the polarity seems negative. Hence to scout relation between an $$aspect$$ aspect and $$context$$ context words present in sentence is much vital. In spite of the exceptional progress, the ASC experiences certain pitfalls as (1) The current attention based methods makes the given aspect to falsely relate syntactically unrelated words as related ones. (2) Single context-independent representation is only achieved by traditional Word2Vec or GloVe based embedding vectors. (3) Sentiments for multiple words that are inconsecutive remain insufficient for CNN based models. A Sparse-Self-Attention-based Gated Recurrent Unit with Aspect Embedding (SSA-GRU-AE) implementing BERT for ASC is proposed to solve these issues. The proposed SSA-GRU-AE mechanism is centralized on various portions of sentence as multiple aspects are taken as input. The experimental analysis on ASC datasets has proved that proposed model enhanced performance on ASC.

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Section: Related Workmentioning

confidence: 99%

“…9. DSMN [41]: The multi-hop attention is guided by dynamically selected context memory which then integrates the aspect information with the memory networks. 10.…”

Section: Interactive Attention Network (Ian): Ian Model [53] Utilizes...mentioning

confidence: 99%

A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification

Dhanith,

Surendiran,

Rohith

et al. 2024

Neural Process Lett

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“…Peiqin Lin et.al [19] introduced the Deep Selective Memory Network (DSMN), an original system for a perspective level feeling arrangement that progressively chooses setting memory for better directing the multi-jump consideration instrument and incorporates between angle data with profound memory organization, was proposed. DSMN centers around unmistakable districts of the setting memory in various memory network layers to catch extensive viewpoint context-setting data by building a particular consideration system dependent on the distance data between a perspective and its specific circumstance.…”

Section: Related Workmentioning

confidence: 99%

Mounting-based knowledge transfer network Model Using Aspect-based sentiment analysis

Sowjanya,

srivi,

Devi

2023

Preprint

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Aspect-based sentiment analysis is a complex subtask of sentiment analysis in which the sentiment polarities of given aspect terms in phrases are determined. In this paper, we suggested a novel Mounting-based knowledge transmission network for aspect-based sentiment analysis. First and foremost, before entering data into models, data preparation (data pre-processing) is crucial, as it contributes to achieving high accuracy. For the textual data, special procedures were required, including tokenization, stop word deletion, and the Weighting Factor, which was all applied in this study. Second, employing feature extraction based on the Aspect term using NB, Modified BERT (MBERT) together with Entropy measure may focus on the critical context words to the given aspect terms in sentences. Finally, Convolution neural network is used to predict the result of sentiment classification aspect polarities as positive or negative.Experimental results show that our proposed approach accomplishes the most extreme accuracy outcome of 92%. Comparable to the TMSC and DNN, which achieves 78%, 72%. The MGAN and CNN had the accuracyworst of 70% and 71%. The HSCN had the very worst accuracy69%.

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“…Memory networks have been applied to aspect-level sentiment classification. It can be used to extract rich aspect-aware context information [18]. Combined with attention mechanism, the performances of memory networks [19,20] can be improved.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble correction model for aspect-level sentiment classification

Zhou

Luo

et al. 2022

Journal of Information Science

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The aspect-level sentiment analysis is widely used in public opinion analysis. However, the problem of context information loss and distortion with the increase of the model depth is rarely considered in previous research. Few studies have attempted to combine the feature extracted from different embedding models. Based on the correction strategy, the ensemble correction (EC) model proposed in this study can correct context information loss and distortion. Based on the ensemble learning strategy and the weight sharing strategy, EC can extract features from different word embedding models and can reduce computational complexity. Experiments on the resturant14, laptop14, resturant16 and twitter datasets show that the accuracies of the EC model are 0.8848, 0.8213, 0.9301 and 0.7731, respectively. The accuracy of the EC model is higher than state-of-the-art models. Ablation studies and case studies are used to verify the model structure. The optimal number of graph convolutional network (GCN) layers is also verified.

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Deep Selective Memory Network With Selective Attention and Inter-Aspect Modeling for Aspect Level Sentiment Classification

Cited by 17 publications

References 29 publications

A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification

A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification

Mounting-based knowledge transfer network Model Using Aspect-based sentiment analysis

Ensemble correction model for aspect-level sentiment classification

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