Data mining analysis of online drug reviews

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dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorZaidi, Abdelhamid
dc.contributor.authorTapales, Catherine P.
dc.contributor.authorNgo-Hoang, Dai-Long
dc.contributor.authorAyaz, Muhammad
dc.contributor.authorDayupay, Johnry P.
dc.contributor.authorAminu Dodo, Yakubu
dc.contributor.authorChaudhury, Sushovan
dc.contributor.authorAdediran, Anthonia Oluwatosin
dc.date.accessioned2023-02-14T12:49:53Z
dc.date.available2023-02-14T12:49:53Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractData mining methods like sentiment analysis provide useful information. This paper examines drug online user reviews. This research predicts user satisfaction with sentiments and applied drugs on effectiveness and side effects using sentiment analysis based on classification and analyzes model transfer across data sources like Emzor and May & Baker data. Online medication review data. Web crawlers was used to collect the ratings and comments of forum members. Emzor Pharmaceutical Company had 463 reviews and May & Baker Pharmaceutical Company had 421 reviews. Data was split 70% for training and 30% for testing. We used sentiment analysis to predict user ratings on overall satisfaction, side effects, and drug efficacy. Emzor data performs better 89.1% in-domain sentiment analysis, while May & Baker data accuracy is 86.90% overall. In cross-data sentiment analysis, the Emzor and May & Baker data performed well when the trained model was applied to side effects. This study acquired data by trawling an internet drug review forum. This study shows that transfer learning can leverage cross-domain similarities to analyze cross-domain sentiment.en_US
dc.identifier.doi10.1109/ICSPC55597.2022.10001810en_US
dc.identifier.endpage251en_US
dc.identifier.scopus2-s2.0-85146731259en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage274en_US
dc.identifier.urihttps://hdl.handle.net/11467/6229
dc.identifier.urihttps://doi.org/10.1109/ICSPC55597.2022.10001810
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 IEEE 10th Conference on Systems, Process and Control, ICSPC 2022 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectData mining; drug; online review; Pharmaceutical; public healthen_US
dc.titleData mining analysis of online drug reviewsen_US
dc.typeConference Objecten_US

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