research-article

Making Decision like Human: Joint Aspect Category Sentiment Analysis and Rating Prediction with Fine-to-Coarse Reasoning

Authors:

Donghong JiAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3042 - 3051

https://doi.org/10.1145/3485447.3512024

Published: 25 April 2022 Publication History

Abstract

Joint aspect category sentiment analysis (ACSA) and rating prediction (RP) is a newly proposed task (namely ASAP) that integrates the characteristics of both fine-grained and coarse-grained sentiment analysis. However, the prior joint models for the ASAP task only consider the shallow interaction between the two granularities. In this work, we gain the inspiration from human intuition, presenting an innovative from-fine-to-coarse reasoning framework for better joint task performance. Our system advances mainly in three aspects. First, we additionally make use of the category label text features, co-encoding them with the input document texts, allowing to accurately capture the key clues of each category. Second, we build a fine-to-coarse hierarchical label graph, modeling the aspect categories and the overall rating as a hierarchical structure for full interaction of the two granularities. Third, we propose to perform global iterative reasoning with a cross-collaboration between the hierarchical label graph and the context graphs, enabling sufficient communication between categories and review contexts. Based on the ASAP dataset, experimental results demonstrate that our proposed framework outperforms state-of-the-art baselines by large margins. Further in-depth analyses prove that our method is effective on addressing both the unbalanced data distribution and the long-text issue.

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Cited By

Nie YFu JZhang YLi C(2024)Modeling implicit variable and latent structure for aspect-based sentiment quadruple extractionNeurocomputing10.1016/j.neucom.2024.127642586(127642)Online publication date: Jun-2024
https://doi.org/10.1016/j.neucom.2024.127642
Sun BSong XLi WLiu LGong GZhao Y(2024)A user review data-driven supplier ranking model using aspect-based sentiment analysis and fuzzy theoryEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107224127:PAOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107224
Fei HChua TLi CJi DZhang MRen Y(2022)On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and TrainingACM Transactions on Information Systems10.1145/356428141:2(1-32)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1145/3564281
Show More Cited By

Index Terms

Making Decision like Human: Joint Aspect Category Sentiment Analysis and Rating Prediction with Fine-to-Coarse Reasoning
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Nie YFu JZhang YLi C(2024)Modeling implicit variable and latent structure for aspect-based sentiment quadruple extractionNeurocomputing10.1016/j.neucom.2024.127642586(127642)Online publication date: Jun-2024
https://doi.org/10.1016/j.neucom.2024.127642
Sun BSong XLi WLiu LGong GZhao Y(2024)A user review data-driven supplier ranking model using aspect-based sentiment analysis and fuzzy theoryEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107224127:PAOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107224
Fei HChua TLi CJi DZhang MRen Y(2022)On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and TrainingACM Transactions on Information Systems10.1145/356428141:2(1-32)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1145/3564281
Xu QLi BLi FFu GJi D(2022)A Dual-Pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoningInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10299259:4Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.102992
Yu PTan WNiu WShi B(2022)Aspect-location attention networks for aspect-category sentiment analysis in social mediaJournal of Intelligent Information Systems10.1007/s10844-022-00760-261:2(395-419)Online publication date: 14-Dec-2022
https://dl.acm.org/doi/10.1007/s10844-022-00760-2

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