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dc.contributor.advisorMajumdar, Mahbub Alam
dc.contributor.authorKarim, Shabab
dc.contributor.authorAbdullah, Tahmid
dc.contributor.authorTayaba, Umme
dc.date.accessioned2018-05-15T06:46:06Z
dc.date.available2018-05-15T06:46:06Z
dc.date.copyright2018
dc.date.issued2018-04
dc.identifier.otherID 14101138
dc.identifier.otherID 14101142
dc.identifier.otherID 14101034
dc.identifier.urihttp://hdl.handle.net/10361/10153
dc.descriptionThis thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 45-47).
dc.description.abstractSocial media has become an integral part in our day to day lives. What we share in these media are what we believe in and give others a window of opportunity to predict what is going on in our mind or how our actions will be in the future. Twitter is amazing at this job because usually people tend to write exactly what they are thinking when the character limit is only hundred and forty characters. This is particularly helpful when we want to analyze the trends of stock market. In this thesis paper, we tried to come up with a solution to better predict stock market trends analyzing the sentiments from twitter feeds obtained from StockTwits.en_US
dc.description.statementofresponsibilityShabab Karim
dc.description.statementofresponsibilityTahmid Abdullah
dc.description.statementofresponsibilityUmme Tayaba
dc.format.extent47 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University theses 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.subjectSentiment analysisen_US
dc.subjectClassi eren_US
dc.subjectRegressionen_US
dc.subjectMachine learningen_US
dc.subjectLogistic regressionen_US
dc.subjectTweetsen_US
dc.titlePredicting stock market trend from twitter feed and building a framework for Bangladeshen_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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