Uddin, JiaRahman, ShaomiHemel, Jonayed NafisAnta, Syed Junayed AhmedAl Muhee, Hossain2018-05-172018-05-1720182018-04ID 14101181ID 14301049ID 14101105ID 14301070http://hdl.handle.net/10361/10163Cataloged from PDF version of thesis.Includes bibliographical references (pages 30-31).This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.Analyzing sentiments has been widely regarded as a popular technique by many researchers, and Twitter nominated the most user-friendly, and reliable social media supplying the stream of sentiments. Among the trendiest topics of discussion in such social platforms, cryptocurrency, and most notably Bitcoin ranks the highest, both providing curiosity as a technology, and a lucrative asset to trade. This thesis studies the correlation among user sentiments from Twitter and the change in price of Bitcoin, to carve out a scalable model by manipulating the category of sentiments as variables and appropriate quantitative machine learning techniques. The work finally achieved a stable precision for determining movement in price, with a high of 75% in accuracy in the short run.31 pagesenBRAC 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.BitcoinPrice fluctuationsUser sentimentsSentiment analysisR programmimg languageThesis