Please use this identifier to cite or link to this item: https://doi.org/10.1145/1458082.1458207
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dc.titleA random walk on the red carpet: Rating movies with user reviews and pagerank
dc.contributor.authorWijaya, D.T.
dc.contributor.authorBressan, S.
dc.date.accessioned2013-07-04T08:25:12Z
dc.date.available2013-07-04T08:25:12Z
dc.date.issued2008
dc.identifier.citationWijaya, D.T.,Bressan, S. (2008). A random walk on the red carpet: Rating movies with user reviews and pagerank. International Conference on Information and Knowledge Management, Proceedings : 951-959. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/1458082.1458207" target="_blank">https://doi.org/10.1145/1458082.1458207</a>
dc.identifier.isbn9781595939913
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41339
dc.description.abstractAlthough PageRank has been designed to estimate the popularity of Web pages, it is a general algorithm that can be applied to the analysis of other graphs other than one of hypertext documents. In this paper, we explore its application to sentiment analysis and opinion mining: i.e. the ranking of items based on user textual reviews. We first propose various techniques using collocation and pivot words to extract a weighted graph of terms from user reviews and to account for positive and negative opinions. We refer to this graph as the sentiment graph. Using PageRank and a very small set of adjectives (such as 'good', 'excellent', etc.) we rank the different items. We illustrate and evaluate our approach using reviews of box office movies by users of a popular movie review site. The results show that our approach is very effective and that the ranking it computes is comparable to the ranking obtained from the box office figures. The results also show that our approach is able to compute context-dependent ratings. Copyright 2008 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/1458082.1458207
dc.sourceScopus
dc.subjectOpinion mining
dc.subjectPagerank
dc.subjectRanking
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/1458082.1458207
dc.description.sourcetitleInternational Conference on Information and Knowledge Management, Proceedings
dc.description.page951-959
dc.identifier.isiutNOT_IN_WOS
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