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Semantics-Driven Implicit Aspect Detection in Consumer Reviews

Authors:

Kim Schouten,

Nienke de Boer,

Tjian Lam,

Marijtje van Leeuwen,

Ruud van Luijk,

Flavius FrasincarAuthors Info & Claims

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Pages 109 - 110

https://doi.org/10.1145/2740908.2742734

Published: 18 May 2015 Publication History

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Abstract

With consumer reviews becoming a mainstream part of e-commerce, a good method of detecting the product or service aspects that are discussed is desirable. This work focuses on detecting aspects that are not literally mentioned in the text, or implicit aspects. To this end, a co-occurrence matrix of synsets from WordNet and implicit aspects is constructed. The semantic relations that exist between synsets in WordNet are exploited to enrich the co-occurrence matrix with more contextual information. Comparing this method with a similar method which is not semantics-driven clearly shows the benefit of the proposed method. Especially corpora of limited size seem to benefit from the added semantic context.

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Cited By

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Soni PRambola R(2022)A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and ScopeIEEE Access10.1109/ACCESS.2022.318320510(63932-63957)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3183205
El Hannach HBenkhalifa M(2020)A New Semantic Relations-Based Hybrid Approach for Implicit Aspect Identification in Sentiment AnalysisJournal of Information & Knowledge Management10.1142/S021964922050019719:03(2050019)Online publication date: 20-Jul-2020
https://doi.org/10.1142/S0219649220500197
Maitama JIdris NAbdi AShuib LFauzi R(2020)A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment AnalysisIEEE Access10.1109/ACCESS.2020.30312178(194166-194191)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3031217
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Index Terms

Semantics-Driven Implicit Aspect Detection in Consumer Reviews
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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Information

Published In

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

May 2015

1602 pages

ISBN:9781450334730

DOI:10.1145/2740908

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2015

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Author Tag

aspect-level sentiment analysis implicit aspects wordnet relations co-occurrence matrix

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WWW '15

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IW3C2

WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Soni PRambola R(2022)A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and ScopeIEEE Access10.1109/ACCESS.2022.318320510(63932-63957)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3183205
El Hannach HBenkhalifa M(2020)A New Semantic Relations-Based Hybrid Approach for Implicit Aspect Identification in Sentiment AnalysisJournal of Information & Knowledge Management10.1142/S021964922050019719:03(2050019)Online publication date: 20-Jul-2020
https://doi.org/10.1142/S0219649220500197
Maitama JIdris NAbdi AShuib LFauzi R(2020)A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment AnalysisIEEE Access10.1109/ACCESS.2020.30312178(194166-194191)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3031217
(2019)Implicit feature identification for opinion miningInternational Journal of Business Information Systems10.5555/3302617.330261930:1(13-30)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3302617.3302619
Tubishat MIdris NAbushariah M(2018)Implicit aspect extraction in sentiment analysisInformation Processing and Management: an International Journal10.1016/j.ipm.2018.03.00854:4(545-563)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1016/j.ipm.2018.03.008
Panchendrarajan RAhamed NSivakumar PMurugaiah BRanathunga SPemasiri ADolog PVojtas PBonchi FHelic D(2017)EateryProceedings of the 28th ACM Conference on Hypertext and Social Media10.1145/3078714.3078737(225-234)Online publication date: 4-Jul-2017
https://dl.acm.org/doi/10.1145/3078714.3078737
Prakhash SNazick APanchendrarajan RBrunthavan MRanathunga SPemasiri A(2016)Categorizing food names in restaurant reviews2016 Moratuwa Engineering Research Conference (MERCon)10.1109/MERCon.2016.7480106(1-5)Online publication date: Apr-2016
https://doi.org/10.1109/MERCon.2016.7480106

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Dynamic model for implicit aspect detection in sentiment analysis using novel aspect pointer compendium

Word embedding-based approach to aspect detection for aspect-based summarization of persian customer reviews

Detection of interference among aspects in aspect oriented modeling

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