| dc.contributor.advisor | Majumdar, Mahbub Alam | |
| dc.contributor.author | Karim, Shabab | |
| dc.contributor.author | Abdullah, Tahmid | |
| dc.contributor.author | Tayaba, Umme | |
| dc.date.accessioned | 2018-05-15T06:46:06Z | |
| dc.date.available | 2018-05-15T06:46:06Z | |
| dc.date.copyright | 2018 | |
| dc.date.issued | 2018-04 | |
| dc.identifier.other | ID 14101138 | |
| dc.identifier.other | ID 14101142 | |
| dc.identifier.other | ID 14101034 | |
| dc.identifier.uri | http://hdl.handle.net/10361/10153 | |
| dc.description | This 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.description | Cataloged from PDF version of thesis. | |
| dc.description | Includes bibliographical references (pages 45-47). | |
| dc.description.abstract | Social 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.statementofresponsibility | Shabab Karim | |
| dc.description.statementofresponsibility | Tahmid Abdullah | |
| dc.description.statementofresponsibility | Umme Tayaba | |
| dc.format.extent | 47 pages | |
| dc.language.iso | en | en_US |
| dc.publisher | BRAC University | en_US |
| dc.rights | BRAC 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.subject | Stock market | en_US |
| dc.subject | Sentiment analysis | en_US |
| dc.subject | Classi er | en_US |
| dc.subject | Regression | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Logistic regression | en_US |
| dc.subject | Tweets | en_US |
| dc.title | Predicting stock market trend from twitter feed and building a framework for Bangladesh | en_US |
| dc.type | Thesis | en_US |
| dc.contributor.department | Department of Computer Science and Engineering, BRAC University | |
| dc.description.degree | B. Computer Science and Engineering | |
