A survey of aspect-based sentiment analysis classification with a focus on graph neural network methods

Zarandi, Akram Karimi; Mirzaei, Sayeh

doi:10.1007/s11042-023-17701-y

Cited by 2 publications

References 146 publications

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Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis

Chen,

Xie,

Qin

et al. 2024

Cogn Comput

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As cognitive-inspired computation approaches, deep neural networks or deep learning (DL) models have played important roles in allowing machines to reach human-like performances in various complex cognitive tasks such as cognitive computation and sentiment analysis. This paper offers a thorough examination of the rapidly developing topic of DL-assisted aspect-based sentiment analysis (DL-ABSA), focusing on its increasing importance and implications for practice and research advancement. Leveraging bibliometric indicators, social network analysis, and topic modeling techniques, the study investigates four research questions: publication and citation trends, scientific collaborations, major themes and topics, and prospective research directions. The analysis reveals significant growth in DL-ABSA research output and impact, with notable contributions from diverse publication sources, institutions, and countries/regions. Collaborative networks between countries/regions, particularly between the USA and China, underscore global engagement in DL-ABSA research. Major themes such as syntax and structure analysis, neural networks for sequence modeling, and specific aspects and modalities in sentiment analysis emerge from the analysis, guiding future research endeavors. The study identifies prospective avenues for practitioners, emphasizing the strategic importance of syntax analysis, neural network methodologies, and domain-specific applications. Overall, this study contributes to the understanding of DL-ABSA research dynamics, providing a roadmap for practitioners and researchers to navigate the evolving landscape and drive innovations in DL-ABSA methodologies and applications.

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Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis

Chen,

Xie,

Qin

et al. 2024

Cogn Comput

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Enhancing Aspect-based Sentiment Analysis through Supervised Contrastive Learning with Induced Tree Structures

Zarandi,

Mirzaei

2024

2024 10th International Conference on Web Research (ICWR)

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A survey of aspect-based sentiment analysis classification with a focus on graph neural network methods

Cited by 2 publications

References 146 publications

Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis

Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis

Enhancing Aspect-based Sentiment Analysis through Supervised Contrastive Learning with Induced Tree Structures

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