Vol.11, No.1, February 2022.                                                                                                                                                                           ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Twitter Sentiment Analysis and Expert Ratings of Initial Coin Offering Fundraising: Evidence from Australia and Singapore Markets

Anchaya Chursook, Ahmad Yahya Dawod, Somsak Chanaim, Nathee Naktnasukanjn, Nopasit Chakpitak

© 2022 Ahmad Yahya Dawod, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 11, Issue 1, Pages 44-55, ISSN 2217-8309, DOI: 10.18421/TEM111-06, February 2022.

Received: 19 October 2021.

Revised:   16 January 2022.
Accepted: 21 January 2022.
Published: 28 February 2022.

Abstract:

Sentiment analysis of Twitter data is quite valuable for determining the market opinion. Twitter sentiment analysis is more challenging than generic sentiment analysis owing to slang and misspellings. The techniques utilized for evaluating the sentiment of tweets that have the greatest importance for the success of an Initial Coin Offering (ICO) are machine learning approaches. In this study, we examined market sentiment and used Expert Ratings to predict the success of ICOs in the Australian and Singapore markets. Based on 68,281 tweets from 57 ICOs across four industries: business services, cryptocurrency, entertainment, and platform. Several classification methods were investigated, including Support Vector Machines (SVMs), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB). The outcomes indicated that sentiment analysis of tweets and expert ratings may be used to forecast the success of an initial coin offering. The results indicate that the suggested model is capable of accurately assessing the tweets of the ICO Successful with a maximum accuracy of about 94.7 % when implementing the Support Vector Machines (SVMs) classifier.

Keywords –Sentiment Analysis, Initial Coin Offerings, Natural Language Processing, Opinion Mining, Tweets.

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