research-article

A Weakly Supervised WordNet-Guided Deep Learning Approach to Extracting Aspect Terms from Online Reviews

Authors:

Lina ZhouAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 11, Issue 3

Article No.: 13, Pages 1 - 22

https://doi.org/10.1145/3399630

Published: 21 July 2020 Publication History

Abstract

The unstructured nature of online reviews makes it inefficient and inconvenient for prospective consumers to research and use in support of purchase decision making. The aspects of products provide a fine-grained meaningful perspective for understanding and organizing review texts. Traditional aspect term extraction approaches rely on discrete language models that treat words in isolation. Despite that continuous-space language models have demonstrated promise in addressing a wide range of problems, their application in aspect term extraction faces significant challenges. For instance, existing continuous-space language models typically require large collections of labeled data, which remain difficult to obtain in many domains. More importantly, previous methods are largely data driven but overlook the role of human knowledge in guiding model development. To address these limitations, this study designs and develops weakly supervised WordNet-guided deep learning to aspect term extraction. The approach draws on deep-level semantic information from WordNet to guide not only the selection representative seed terms but also the pruning of aspect candidate terms. The weak supervision is provided by a very small set of labeled data. We conduct a comprehensive evaluation of the proposed method using both direct and indirect methods. The evaluation results with Yelp restaurant reviews demonstrate that our proposed method consistently outperforms all baseline methods including discrete models and the state-of-the-art continuous-space language models for aspect term extraction across both direct and indirect evaluations. The research findings have broad research, technical, and practical implications for various stakeholders of online reviews.

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Cited By

Shang WChai JCao JLei XZhu HFan YDing W(2024)Aspect-level sentiment analysis based on aspect-sentence graph convolution networkInformation Fusion10.1016/j.inffus.2023.102143104:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.inffus.2023.102143
Alotaibi YNoman Malik MHayat Khan HBatool Aul Islam SAlsufyani AAlghamdi S(2021)Suggestion Mining from Opinionated Text of Big Social Media DataComputers, Materials & Continua10.32604/cmc.2021.01672768:3(3323-3338)Online publication date: 2021
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Index Terms

A Weakly Supervised WordNet-Guided Deep Learning Approach to Extracting Aspect Terms from Online Reviews

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Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 11, Issue 3

Special Section on WITS 2018 and Regular Articles

September 2020

140 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3407737

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

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Copyright © 2020 ACM.

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Publication History

Published: 21 July 2020

Accepted: 01 May 2020

Revised: 01 November 2019

Received: 01 May 2019

Published in TMIS Volume 11, Issue 3

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Cited By

Shang WChai JCao JLei XZhu HFan YDing W(2024)Aspect-level sentiment analysis based on aspect-sentence graph convolution networkInformation Fusion10.1016/j.inffus.2023.102143104:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.inffus.2023.102143
Alotaibi YNoman Malik MHayat Khan HBatool Aul Islam SAlsufyani AAlghamdi S(2021)Suggestion Mining from Opinionated Text of Big Social Media DataComputers, Materials & Continua10.32604/cmc.2021.01672768:3(3323-3338)Online publication date: 2021
https://doi.org/10.32604/cmc.2021.016727
Wang SZhang XZhang Y(2021)DSSAE: Deep Stacked Sparse Autoencoder Analytical Model for COVID-19 Diagnosis by Fractional Fourier EntropyACM Transactions on Management Information Systems10.1145/345135713:1(1-20)Online publication date: 5-Oct-2021
https://dl.acm.org/doi/10.1145/3451357
Liu YLi FJi D(2021)Aspect-Based Pair-Wise Opinion Generation in Chinese automotive reviews: Design of the task, dataset and modelInformation Processing & Management10.1016/j.ipm.2021.10272958:6(102729)Online publication date: Nov-2021
https://doi.org/10.1016/j.ipm.2021.102729
Kanika Chakraverty SChakraborty PAggarwal AMadan MGupta G(2021)Enriching WordNet with Subject Specific Out of Vocabulary Terms Using Existing OntologyData Engineering for Smart Systems10.1007/978-981-16-2641-8_19(205-212)Online publication date: 14-Nov-2021
https://doi.org/10.1007/978-981-16-2641-8_19

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