Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/77643
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dc.contributor.authorAnchaya Chursooken_US
dc.contributor.authorNathee Naktnasukanjnen_US
dc.contributor.authorSomsak Chaimaimen_US
dc.contributor.authorPiyachat Udomwongen_US
dc.contributor.authorJutharut Chatsirikulen_US
dc.contributor.authorNopasit Chakpitaken_US
dc.date.accessioned2022-10-16T08:09:29Z-
dc.date.available2022-10-16T08:09:29Z-
dc.date.issued2020-11-18en_US
dc.identifier.other2-s2.0-85103738999en_US
dc.identifier.other10.1109/iSAI-NLP51646.2020.9376810en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103738999&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/77643-
dc.description.abstractInitial coin offerings (ICOs) are a new fundraising method for businesses, companies or entrepreneurs through a smart contract. The ICO fundraising model was one of the most popular FinTechs from 2016 to 2018, as the most supportive tool for startups in countries with limitations on initial public offerings (IPOs). Among ICOs ended in 2018, the leading sectors of funds raised included cryptocurrencies, business platforms, and business service projects. An ICO whitepaper contains primary business data that can assist in decision-making for investment. Market sentiment was used to predict the success of ICOs based in Australia and Singapore markets. Results showed that sentiment analysis on tweets can be used to predict ICO success.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.subjectPhysics and Astronomyen_US
dc.titleCan Tweets predict ICO success? Sentiment Analysis for Success of ICO Whitepaper: Evidence from Australia and Singapore Marketsen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleProceedings - 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2020en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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