Incorporating Reviewer and Product Information for Review Rating Prediction

Simple item page
dc.contributor.authorLi, Fangtao
dc.contributor.authorLiu, Nathan
dc.contributor.authorJin, Hongwei
dc.contributor.authorZhao, Kai
dc.contributor.authorYang, Qiang
dc.contributor.editorWalsh, Toby
dc.date.accessioned2012-05-22T19:52:03Z
dc.date.available2012-05-22T19:52:03Z
dc.date.copyright2011
dc.date.issued2011-07
dc.descriptionProceedings of the Twenty-Second International Joint Conference on Artificial Intelligenceen
dc.description.abstractTraditional sentiment analysis mainly considers binary classifications of reviews, but in many real-world sentiment classification problems,nonbinary review ratings are more useful. This is especially true when consumers wish to compare two products, both of which are not negative. Previous work has addressed this problem by extracting various features from the review text for learning a predictor. Since the same word may have different sentiment effects when used by different reviewers on different products, we argue that it is necessary to model such reviewer and product dependent effects in order to predict review ratings more accurately. In this paper, we propose a novel learning framework to incorporate reviewer and product information into the text based learner for rating prediction. The reviewer, product and text features are modeled as a three-dimension tensor. Tensor factorization techniques can then be employed to reduce the data sparsity problems. We perform extensive experiments to demonstrate the effectiveness of our model, which has a significant improvement compared to state of the art methods, especially for reviews with unpopular products and inactive reviewers.en
dc.formatTexten
dc.format.extent1 digital file (p. 1820-1825 : ill.)en
dc.identifier.citationLi, F., Liu, N., Jin, H., Zhao, K., & Yang, Q. (2011). Incorporating Reviewer and Product Information for Review Rating Prediction. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, ESP. (p.1820-1825). AAAI / International Joint Conferences on Artificial Intelligence Press. doi:10.5591/978-1-57735-516-8/IJCAI11-305en
dc.identifier.isbn978-1-57735-513-7
dc.identifier.urihttp://hdl.handle.net/10625/49045
dc.language.isoen
dc.publisherAAAI / International Joint Conferences on Artificial Intelligence Press, Menlo Park, Californiaen
dc.subjectBINARY DATAen
dc.subjectPRODUCT REVIEWSen
dc.subjectTENSOR ANALYSISen
dc.subjectMACHINE LEARNINGen
dc.subjectSENTIMENT ANALYSISen
dc.titleIncorporating Reviewer and Product Information for Review Rating Predictionen
dc.typeConference Paperen
idrc.copyright.holderInternational Joint Conferences on Artificial Intelligence
idrc.dspace.accessIDRC Onlyen
idrc.noaccessDue to copyright restrictions the full text of this research output is not available in the IDRC Digital Library or by request from the IDRC Library. / Compte tenu des restrictions relatives au droit d'auteur, le texte intégral de cet extrant de recherche n'est pas accessible dans la Bibliothèque numérique du CRDI, et il n'est pas possible d'en faire la demande à la Bibliothéque du CRDI.en
idrc.project.componentnumber104519006
idrc.project.number104519
idrc.project.titleInternational Research Chairs Initiative (IRCI)en
idrc.rims.adhocgroupIDRC SUPPORTEDen

Files

Collections

IDRC Research Results / Résultats de recherches du CRDI
2010-2019 / Années 2010-2019
Breaking the barriers to Internet access / Faire tomber les obstacles entravant l’accès à Internet