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.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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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)
- Urheber
- DOI
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10.1515/comp-2020-0148
- URN
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urn:nbn:de:101:1-2410301437087.492287882384
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:25 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Younis, Umair
- Asghar, Muhammad Zubair
- Khan, Adil
- Khan, Alamsher
- Iqbal, Javed
- Jillani, Nosheen
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