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dc.contributor.authorRong, Hao
dc.date.accessioned2018-10-23T13:36:02Z
dc.date.available2018-10-23T13:36:02Z
dc.date.issued2018-08-30
dc.identifier.otherRong_cornell_0058O_10364
dc.identifier.otherhttp://dissertations.umi.com/cornell:10364
dc.identifier.otherbibid: 10489855
dc.identifier.urihttps://hdl.handle.net/1813/59757
dc.description.abstractThe housing bubble is one of the most urgent social problems to address in China. To guide healthy investment behavior and make effective regulatory policy, it is essential to understand how real estate market discussion shifts correspond to the changes in market conditions and social values. By understanding market discussion, not only can we evaluate the efficiency of the current policy, but we can also make better policy decisions in the future. Since most of the market discussion from certain individuals or organizations are posted online in the form of articles, text mining could be a potent tool in extracting information in order to better comprehend public opinions. This research focuses on obtaining valuable information from text data in social media, organizing and structuring text data, and making convincing statistical inferences on the relationship between online discussions and the actual situation of the real estate market.
dc.language.isoen_US
dc.subjectInformation technology
dc.subjectTopic Modeling
dc.subjectPublic policy
dc.subjectGranger Causality
dc.subjectHousing Price
dc.subjectText Mining
dc.subjectArea planning & development
dc.subjectSocial Media
dc.titleUse of Text Mining to Understand Real Estate Trends and Market Discussion on Social Media
dc.typedissertation or thesis
thesis.degree.disciplineRegional Science
thesis.degree.grantorCornell University
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Regional Science
dc.contributor.chairDonaghy, Kieran Patrick
dc.contributor.committeeMemberMimno, David
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/X4N29V69


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