Sentiment Analysis Comparative Analysis of Sentiment Analysis Using the Support Vector Machine and Naive Bayes Algorithm on Cryptocurrencies

CRISP - DM

Authors

  • Nicholas Nicholas Student
  • Rudi Sutomo

DOI:

https://doi.org/10.53748/jmis.v1i3.22

Abstract

Objective – Cryptocurrency is growing overtime even being adopted as a legal money in a country out there. Besides can be used as a money, cryptocurrency also can be used as a digital goods to be trade and investment assets. To do some investing in cryptocurrency, there’s a need to evaluate the fundamental and sentiment of that cryptocurrency. This study aims to evaluate cryptocurrency based on responses of Twitter user.
Methodology – The Algorithms used in this sentiment analysis study are Support Vector Machine and Naïve Bayes because it’s already proven that these 2 algorithm able to give a good accuracy and performance and using CRISP – DM framework for the study flow.
Findings – This research predicts the sentiment for Bitcoin, Ethereum, Binance Coin, Dogecoin, and Ripple using the CRISP - DM method and using Support Vector Machine and Naïve Bayes algorithm.
Novelty – This study calculate the sentiment on cryptocurrency using Rapidminer tools.
Limitations - This study uses Bitcoin, Ethereum, Binance Coin, Dogecoin, and Ripple using tools such as rapidminer
Keywords — Cryptocurrency, Naïve Bayes, Sentiment Analysis, Support Vector Machine

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Published

31-12-2021

How to Cite

Nicholas, N., & Sutomo, R. (2021). Sentiment Analysis Comparative Analysis of Sentiment Analysis Using the Support Vector Machine and Naive Bayes Algorithm on Cryptocurrencies: CRISP - DM. Journal of Multidisciplinary Issues, 1(3), 2–19. https://doi.org/10.53748/jmis.v1i3.22

Issue

Vol. 1 No. 3 (2021): Journal of Multidisciplinary Issues

Section

Articles

License

Copyright (c) 2021 Nicholas Nicho, Rudi Sutomo

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