Applying Machine Learning Techniques for Performing Comparative Opinion Mining
Abstract: In recent times, comparative opinion mining applications have attracted both individuals and business organizations to compare the strengths and weakness of products. Prior works on comparative opinion mining have focused on applying a single classifier, limited comparative opinion labels, and limited dataset of product reviews, resulting in degraded performance for classifying comparative reviews. In this work, we perform multi-class comparative opinion mining by applying multiple machine learning classifiers using an increased number of comparative opinion labels (9 classes) on 4 datasets of comparative product reviews. The experimental results show that Random Forest classifier has outperformed the comparing algorithms in terms of improved accuracy, precision, recall and f-measure.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Applying Machine Learning Techniques for Performing Comparative Opinion Mining ; volume:10 ; number:1 ; year:2020 ; pages:461-477 ; extent:17
Open computer science ; 10, Heft 1 (2020), 461-477 (gesamt 17)
- Creator
- DOI
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10.1515/comp-2020-0148
- URN
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urn:nbn:de:101:1-2410301437087.492287882384
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:25 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Younis, Umair
- Asghar, Muhammad Zubair
- Khan, Adil
- Khan, Alamsher
- Iqbal, Javed
- Jillani, Nosheen
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