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

N. Nicholas, Rudi Sutomo
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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 rapidminerKeywords — Cryptocurrency, Naïve Bayes, Sentiment Analysis, Support Vector Machine
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基于支持向量机和朴素贝叶斯算法的加密货币情感分析比较分析
目标-加密货币正在不断增长,甚至在一个国家被采纳为合法货币。加密货币除了可以作为货币使用外,还可以作为数字商品进行贸易和投资资产。要对加密货币进行投资,有必要评估这种加密货币的基本面和情绪。本研究旨在基于Twitter用户的反应来评估加密货币。方法-在此情感分析研究中使用的算法是支持向量机和Naïve贝叶斯,因为已经证明这两种算法能够提供良好的准确性和性能,并使用CRISP - DM框架进行研究流程。研究结果-本研究使用CRISP - DM方法并使用支持向量机和Naïve贝叶斯算法预测比特币,以太坊,币安币,狗狗币和瑞波币的情绪。新颖性-本研究使用Rapidminer工具计算对加密货币的情绪。限制-本研究使用比特币,以太坊,币安币,狗狗币和Ripple使用rapidminerKeywords - Cryptocurrency, Naïve贝叶斯,情绪分析,支持向量机等工具
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