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Predicting stock market movement using sentiment analysis of twitter feed

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dc.contributor.advisor Majumdar, Dr. Mahbub Alam Chakraborty, Pranjal Rony, Rashad Al Hasan Pria, Ummay Sani 2017-05-30T06:46:11Z 2017-05-30T06:46:11Z 2017 2017-04-16
dc.identifier.other ID 13301071
dc.identifier.other ID 13301033
dc.identifier.other ID 13301055
dc.description This 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.description Cataloged from PDF version of thesis report.
dc.description Includes bibliographical references (page 36-37).
dc.description.abstract Collecting 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.statementofresponsibility Pranjal Chakraborty
dc.description.statementofresponsibility Rashad Al Hasan Rony
dc.description.statementofresponsibility Ummay Sani Pria
dc.format.extent 37 pages
dc.language.iso en en_US
dc.publisher BRAC University en_US
dc.rights BRAC 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.subject Stock market en_US
dc.subject Twitter feed en_US
dc.subject Sentiment analysis en_US
dc.subject Decision tree en_US
dc.subject Random forest en_US
dc.subject Boosted tree en_US
dc.subject Opinion mining en_US
dc.subject Machine Learning en_US
dc.title Predicting stock market movement using sentiment analysis of twitter feed en_US
dc.type Thesis en_US
dc.contributor.department Department of Computer Science and Engineering, BRAC University B. Computer Science and Engineering

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