Multiple-element joint detection for Aspect-Based Sentiment Analysis

Wu, Chao; Xiong, Qingyu; Yi, Hualing; Yu, Yang; Zhu, Qiwu; Gao, Min; Chen, Jie

doi:10.1016/j.knosys.2021.107073

Cited by 39 publications

(18 citation statements)

References 14 publications

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“…Sun et al [33] proposed a convolutionoverdependencytree which utilizes the Bi-LSTM and captures the important features from the sentence that could be fed as input to GCN which then transfers information from the opinion words to aspect words. Liang et al [44] proposed an interactive multitask learning model by incorporating a new message passing mechanism which utilizes dependency relation embedded GCN to completely exploit the syntactic knowledge for end-to-end ABSA. Wu et al [45] introduced a GCN with attention utilizing BERT to capture the relation between aspect and its context, where attention controls information flow in the GCN.…”

Section: Related Workmentioning

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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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%

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

Dhanith,

Surendiran,

Rohith

et al. 2024

Neural Process Lett

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“…In this example, the triplet is "battery life-battery-positive." Wan et al (2020) proposed the ACSTE task, and subsequently improved upon by MEJD (Wu et al, 2021a), PARAPHRASE-T5 (Zhang et al, 2021a) and GAS-T5 (Zhang et al, 2021c) . "…”

Section: Aspect Category Sentiment Triplet Extraction (Acste)mentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Quadruple Extraction with Implicit Aspects and Opinions

Zhang

Cheah

Alyasiri

et al. 2023

Preprint

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Quadruple sentiment analysis is an essential task in aspect-based sentiment analysis that aims to understand people's viewpoints by analyzing four elements in text: category, aspect, opinion, and sentiment polarity. In recent years, quadruple sentiment analysis tasks have received extensive research attention for capturing comprehensive sentiment information in sentences. This paper focuses on the latest progress in quadruple sentiment analysis tasks. Additionally, the paper provides a summary of the application of methods based on pre-trained language models and datasets in quadruple sentiment analysis with implicit aspects and opinions. It discusses techniques for constructing more practical quadruple sentiment analysis systems in generative large model scenarios. The paper's final section outlines quadruple sentiment analysis research's current challenges and future directions. Moreover, the paper suggests that using advanced language models, such as ChatGPT, holds promise for improving the accuracy and efficiency of quadruple sentiment analysis. Overall, this paper sheds light on the importance of quadruple sentiment analysis and highlights opportunities for further research in this field.

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“…TwoStage [12], JET [91], GTS [75], OTE-MTL [92], BMRC [93] Dual-MRC [94], GAS [95], Gen-ABSA [96], Span-ASTE [97], NAG-ASTE [98], PASTE [99] Aspect-Category-Sentiment Detection (ACSD) TAS-BERT [46], MEJD [100], GAS [95], Paraphrase [7] Quad Extraction Aspect Sentiment Quad Prediction (ASQP) Extract-Classify-ACOS [101], Paraphrase [7] Fig. 2.…”

Section: Aspect Sentiment Triplet Extraction (Aste)mentioning

confidence: 99%

“…The overall training objective can be the combined loss of these two subtasks. Following this direction, Wu et al [100] propose a model called MEJD which handles the task by using the sentence and a specific aspect category as input, then the remaining problems becomes: (1) predicting the sentiment polarity for the given category (i.e., a SeqClass problem), and (2) extract the corresponding aspect terms if exist (i.e., a TokenClass problem). Since a specific aspect category may not always exist in the concerned sentence, MEJD adds an extra dimension "N/A" in the SeqClass task, sharing the similar idea of the "add-one-dimension" method [88] introduced in Sec 4.3.…”

Section: Aspect-category-sentiment Detection (Acsd)mentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Zhang¹,

Li²,

Yang³

et al. 2022

Preprint

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As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks have been introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA.

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Multiple-element joint detection for Aspect-Based Sentiment Analysis

Cited by 39 publications

References 14 publications

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

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

A Survey on Aspect-Based Sentiment Quadruple Extraction with Implicit Aspects and Opinions

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

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