Sentiment analysis with ensemble and machine learning methods in multi-domain datasets

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dc.authorscopusid57203003458en_US
dc.authorscopusid56495320500en_US
dc.contributor.authorBaşarslan, M.S.
dc.contributor.authorKayaalp, F.
dc.date.accessioned2024-08-23T16:07:28Z
dc.date.available2024-08-23T16:07:28Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThe first place to get ideas on all the activities considered to occur in everyday life was the comments on the websites. This is an area that deals with these interpretations in the natural language processing, which is a sub-branch of artificial intelligence. Sentiment analysis studies, which is a task of natural language processing are carried out to give people an idea and even guide them with such comments. In this study, sentiment analysis was implemented on public user feedback on websites in two different areas. TripAdvisor dataset includes positive or negative user comments about hotels. And Rotten Tomatoes dataset includes positive (fresh) or negative (rotten) user comments about films. Sentiments analysis on datasets have been carried out by using Word2Vec word embedding model, which learns the vector representations of each word containing the positive or negative meaning of the sentences, and the Term Frequency Inverse Document Frequency text representation model with four machine learning methods (Naïve Bayes-NB, Support Vector Machines-SVM, Logistic Regression-LR, K-Nearest Neighbour-kNN) and two ensemble learning methods (Stacking, Majority Voting-MV). Accuracy and F-measure is used as a performance metric experiments. According to the results, Ensemble learning methods have shown better results than single machine learning algorithms. Among the overall approaches, MV outperformed Stacking. © Author(s) 2023.en_US
dc.identifier.doi10.31127/tuje.1079698
dc.identifier.endpage148en_US
dc.identifier.issn2587-1366
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85161403278en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage141en_US
dc.identifier.trdizinid1181109en_US
dc.identifier.urihttps://doi.org/10.31127/tuje.1079698
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1181109
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14663
dc.identifier.volume7en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherMurat Yakaren_US
dc.relation.ispartofTurkish Journal of Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEnsemble Learningen_US
dc.subjectMachine Learningen_US
dc.subjectSentiment Analysisen_US
dc.subjectText Representationen_US
dc.titleSentiment analysis with ensemble and machine learning methods in multi-domain datasetsen_US
dc.typeArticleen_US

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