research-article

Ontology-based Aspect Extraction for an Improved Sentiment Analysis in Summarization of Product Reviews

Authors:

Ali Marstawi,

Nurfadhlina Mohd Sharef,

Teh Noranis Mohd Aris,

Aida MustaphaAuthors Info & Claims

ICCMS '17: Proceedings of the 8th International Conference on Computer Modeling and Simulation

Pages 100 - 104

https://doi.org/10.1145/3036331.3036362

Published: 20 January 2017 Publication History

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Abstract

Current approaches in aspect-based sentiment analysis ignore or neutralize unhandled issues emerging from the lexicon-based scoring (i.e., SentiWordNet), whereby lexical sentiment analysis only classifies text based on affect word presence and word count are limited to these surface features. This is coupled with considerably low detection rate among implicit concepts in the text. To address this issues, this paper proposed the use of ontology to i) enhance aspect extraction process by identifying features pertaining to implicit entities, and ii) eliminate lexicon-based sentiment scoring issues which, in turn, improve sentiment analysis and summarization accuracy. Concept-level sentiment analysis aims to go beyond word-level analysis by employing ontologies which act as a semantic knowledge base rather than the lexicon. The outcome is an Ontology-Based Product Sentiment Summarization (OBPSS) framework which outperformed other existing summarization systems in terms of aspect extraction and sentiment scoring. The improved performance is supported by the sentence-level linguistic rules applied by OBPSS in providing a more accurate sentiment analysis.

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Cited By

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Singh UAbhishek KAzad H(2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3652149
Darshini PShekhawat H(2024)Design of a CWA-wbiLSTM Model for Aspect based Sentiment Classification for Product ReviewsWireless Personal Communications10.1007/s11277-024-11690-3139:3(1709-1733)Online publication date: 19-Dec-2024
https://doi.org/10.1007/s11277-024-11690-3
Hua YDenny PWicker JTaskova K(2024)A systematic review of aspect-based sentiment analysis: domains, methods, and trendsArtificial Intelligence Review10.1007/s10462-024-10906-z57:11Online publication date: 17-Sep-2024
https://doi.org/10.1007/s10462-024-10906-z
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ICCMS '17: Proceedings of the 8th International Conference on Computer Modeling and Simulation

January 2017

207 pages

ISBN:9781450348164

DOI:10.1145/3036331

© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Central Queensland University
University of Canberra: University of Canberra

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Association for Computing Machinery

New York, NY, United States

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Published: 20 January 2017

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ICCMS '17: 8th International Conference on Computer Modeling and Simulation

January 20 - 23, 2017

Canberra, Australia

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Singh UAbhishek KAzad H(2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
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Darshini PShekhawat H(2024)Design of a CWA-wbiLSTM Model for Aspect based Sentiment Classification for Product ReviewsWireless Personal Communications10.1007/s11277-024-11690-3139:3(1709-1733)Online publication date: 19-Dec-2024
https://doi.org/10.1007/s11277-024-11690-3
Hua YDenny PWicker JTaskova K(2024)A systematic review of aspect-based sentiment analysis: domains, methods, and trendsArtificial Intelligence Review10.1007/s10462-024-10906-z57:11Online publication date: 17-Sep-2024
https://doi.org/10.1007/s10462-024-10906-z
Hayatin NAlias SHung L(2024)Trends and challenges in sentiment summarization: a systematic review of aspect extraction techniquesKnowledge and Information Systems10.1007/s10115-024-02075-w66:7(3671-3717)Online publication date: 9-May-2024
https://doi.org/10.1007/s10115-024-02075-w
Li JLi ZMa XZhao QZhang CYu G(2023)Sentiment Analysis on Online Videos by Time-Sync CommentsEntropy10.3390/e2507101625:7(1016)Online publication date: 2-Jul-2023
https://doi.org/10.3390/e25071016
Ullah AKhan SNawi N(2022)Review on sentiment analysis for text classification techniques from 2010 to 2021Multimedia Tools and Applications10.1007/s11042-022-14112-382:6(8137-8193)Online publication date: 1-Dec-2022
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Wahid JHussain SWang HWu ZShi LGao Y(2021)Aspect oriented Sentiment classification of COVID-19 twitter data; an enhanced LDA based text analytic approach2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)10.1109/ICCEAI52939.2021.00054(271-275)Online publication date: Aug-2021
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Chechneva N(2020)Simple and Efficient Approach to the Aspect Extraction from Customers’Product Reviews2020 26th Conference of Open Innovations Association (FRUCT)10.23919/FRUCT48808.2020.9087546(1-7)Online publication date: Apr-2020
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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
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Gandhi HAttar V(2020)Extracting Aspect Terms using CRF and Bi-LSTM ModelsProcedia Computer Science10.1016/j.procs.2020.03.301167(2486-2495)Online publication date: 2020
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