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dc.contributor.advisorMajumdar, Dr. Mahbub Alam
dc.contributor.authorChakraborty, Pranjal
dc.contributor.authorRony, Rashad Al Hasan
dc.contributor.authorPria, Ummay Sani
dc.date.accessioned2017-05-30T06:46:11Z
dc.date.available2017-05-30T06:46:11Z
dc.date.copyright2017
dc.date.issued2017-04-16
dc.identifier.otherID 13301071
dc.identifier.otherID 13301033
dc.identifier.otherID 13301055
dc.identifier.urihttp://hdl.handle.net/10361/8206
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 36-37).
dc.description.abstractCollecting opinions of mass people through the social networking sites has become easy and handy now-a-days. These opinions show the sentimental state of a large number of people, which, according to behavioral economics, will give us the idea about their decision-making process. Twitter is a very popular social networking site and by opinion mining, it is possible to get the sentimental state from the tweets. Moreover, there are publicly available data of Twitter. In this thesis paper, we will try to correlate between this sentimental data and stock market data to predict future movement of stock market.en_US
dc.description.statementofresponsibilityPranjal Chakraborty
dc.description.statementofresponsibilityRashad Al Hasan Rony
dc.description.statementofresponsibilityUmmay Sani Pria
dc.format.extent37 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectStock marketen_US
dc.subjectTwitter feeden_US
dc.subjectSentiment analysisen_US
dc.subjectDecision treeen_US
dc.subjectRandom foresten_US
dc.subjectBoosted treeen_US
dc.subjectOpinion miningen_US
dc.subjectMachine Learningen_US
dc.titlePredicting stock market movement using sentiment analysis of twitter feeden_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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