Show simple item record

dc.contributor.authorSadiq, S
dc.contributor.authorUmer, M
dc.contributor.authorUllah, S
dc.contributor.authorMirjalili, S
dc.contributor.authorRupapara, V
dc.contributor.authorNappi, M
dc.date.accessioned2021-06-10T00:18:07Z
dc.date.available2021-06-10T00:18:07Z
dc.date.issued2021
dc.identifier.issn0957-4174
dc.identifier.doi10.1016/j.eswa.2021.115111
dc.identifier.urihttp://hdl.handle.net/10072/405047
dc.description.abstractNowadays online reviews play a significant role in influencing the decision of consumers. Consumers show their experience and information about product quality in their reviews. Product Reviews from Amazon to Restaurant Reviews from Yelp are facing problems with fake reviews and fake numeric ratings. Online reviews typically consist of qualitative (text format) and quantitative (rating) formats. In the case of Google Play store fake numeric ratings can play a big role in the success of apps. People tend to believe that a high-star rating may be significantly attached with a good review. However, user star level rating information does not usually match with text format of review. Despite many efforts to resolve this issue, Apple App Store and Google Play Store are still facing this problem. This study proposes a novel Google App numeric reviews & ratings contradiction prediction framework using Deep Learning approaches. The framework consists of two phases. In the first phase, the polarity of reviews are predicted using sentiment analysis tool to build ground truth. In the second phase, star ratings are predicted from text format of reviews after training deep learning models on ground truth obtained in the first phase. Experimental results demonstrate that based on actual user reviews the proposed framework significantly predicts unbiased star rating of app.
dc.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom115111
dc.relation.ispartofjournalExpert Systems with Applications
dc.relation.ispartofvolume181
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode40
dc.titleDiscrepancy detection between actual user reviews and numeric ratings of Google App store using deep learning
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSadiq, S; Umer, M; Ullah, S; Mirjalili, S; Rupapara, V; Nappi, M, Discrepancy detection between actual user reviews and numeric ratings of Google App store using deep learning, Expert Systems with Applications, 2021, 181, pp. 115111
dc.date.updated2021-06-09T06:16:56Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record