Analyzing Causality between Actual Stock Prices and User-weighted Sentiment in Social Media for Stock Market Prediction

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dc.contributor.advisorLeung, Henry
dc.contributor.authorPark, Jin-Tak
dc.contributor.committeememberFar, Behrouz
dc.contributor.committeememberRuhe, Guenther
dc.date.accessioned2016-10-04T15:35:25Z
dc.date.available2016-10-04T15:35:25Z
dc.date.issued2016
dc.date.submitted2016en
dc.description.abstractIn this thesis, an improved sentiment analysis algorithm is proposed which reflects the impact of user, and to analyze whether public sentiment calculated by the proposed algorithm can contribute to stock prediction. The proposed sentiment analysis algorithm reflects the factors of Twitter which are relevant to users’ authority to calculate sentiment weight of each message that is different from existing sentiment analysis algorithms. Linear and nonlinear prediction models are constructed to forecast future stock prices of selected companies. The proposed algorithm is applied to both linear and nonlinear prediction models and comparisons of prediction accuracy with the existing sentiment analysis algorithm are performed. To support the approach of the proposed algorithm that the authoritative users affect the other users, causal relationship between them is figured out through Granger Causality analysis. Further analysis is also provided to find causal relationship between public sentiment and the actual changes of the stock prices.en_US
dc.identifier.citationPark, J. (2016). Analyzing Causality between Actual Stock Prices and User-weighted Sentiment in Social Media for Stock Market Prediction (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24828en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/24828
dc.identifier.urihttp://hdl.handle.net/11023/3374
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectEngineering--Electronics and Electrical
dc.subject.classificationData Miningen_US
dc.subject.classificationBig Dataen_US
dc.subject.classificationSentiment Analysisen_US
dc.subject.classificationNatural Language Processingen_US
dc.subject.classificationMachine Learningen_US
dc.subject.classificationStock Market Predictionen_US
dc.titleAnalyzing Causality between Actual Stock Prices and User-weighted Sentiment in Social Media for Stock Market Prediction
dc.typemaster thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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