Examining the Impact of Feature Selection on Classification of User Reviews in Web Pages

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dc.authorid0000-0002-3971-2676
dc.authorid0000-0003-4351-2244
dc.authorscopusid54783608800
dc.authorscopusid57194265151
dc.authorwosidOZHAN, Erkan/N-8743-2016
dc.authorwosidUzun, Erdinç/AAG-5529-2019
dc.contributor.authorUzun, Erdinç
dc.contributor.authorÖzhan, Erkan
dc.date.accessioned2022-05-11T14:15:53Z
dc.date.available2022-05-11T14:15:53Z
dc.date.issued2018
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEY
dc.description.abstractThe user reviews in web pages can provide useful information about the content of the web page for text processing applications. Automatically extracting data from a web page is a crucial process for these applications. One of the used methods in this process is to construct a learning model with an appropriate classification method using features that are derived from data. However, some features can be either redundant or irrelevant for this model. In this study, an imbalanced dataset including 47 shallow text features obtained from web pages is utilized for extracting of the user reviews. Then, various well-known feature selection techniques are applied to reduce the number of these features. The effects of this reduction on the classification methods are also examined. The experimental results indicate that approximately half of the features are sufficient for the classification task. Additionally, the AdaBoost classifier gives the best results concerning precision of about 0.930 for the review layout prediction.
dc.description.sponsorshipInonu Univ, Comp Sci Dept, IEEE Turkey Sect, Anatolian Sci
dc.description.sponsorshipNamik Kemal University Research FundNamik Kemal University
dc.description.sponsorshipThe authors acknowledge the support received from the Namik Kemal University Research Fund.
dc.identifier.isbn978-1-5386-6878-8
dc.identifier.scopus2-s2.0-85062568576
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6107
dc.identifier.wosWOS:000458717400054
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUzun, Erdinç
dc.institutionauthorÖzhan, Erkan
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2018 International Conference on Artificial Intelligence and Data Processing (Idap)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectweb data extraction
dc.subjectfeature selection
dc.subjectclassification methods
dc.subjectreview layout detection
dc.subjectimbalanced dataset
dc.subjectHybrid Approach
dc.titleExamining the Impact of Feature Selection on Classification of User Reviews in Web Pages
dc.typeConference Object

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