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首页> 外文期刊>Journal of Theoretical and Applied Information Technology >USING DATA MINING ALGORITHM FOR SENTIMENT ANALYSIS OF USERS OPINIONS ABOUT BITCOIN CRYPTOCURRENCY
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USING DATA MINING ALGORITHM FOR SENTIMENT ANALYSIS OF USERS OPINIONS ABOUT BITCOIN CRYPTOCURRENCY

机译：使用数据挖掘算法对比特币密码币用户意见进行情感分析

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摘要

Cryptocurrency has turned out to be one of the most significant currencies in the recent times due to their secureness, ease and value. Among the other cryptocurrencies available in the market, Bitcoin cryptocurrency is the most valuable and famous currency. A large number of people discuss about the Bitcoin currency on the internet and social media platforms as well. These discussions help in determining the importance of Bitcoin in terms of users discussions about Bitcoin and can help in determining the value of Bitcoin in terms of people point of views about the topic. In this paper, sentiment analysis of the tweets of users on the topic of Bitcoin has been carried out. For this purpose, real-world twitter data set of Bitcoins is used. The data set has been divided into five separate sections for better comparative analysis, including overall extensive data analysis regarding tweets, retweets, tweets with mentions, tweets containing external links and also about the users who discuss regarding cryptocurrency of Bitcoin. A framework for sentiment analysis is proposed on the basis of Naive Bayes sentiment classification algorithms which is widely used as a better option for text data. The proposed framework is capable to perform sentiment analysis of the tweets data. The results resemble that users opinions are on the high positive side about Bitcoins and people mostly people represent the positive sentiment about Bitcoins. The results are evaluated using the standard performance evaluation measures including precision, recall, f1-score and accuracy.
机译：由于其安全性，便捷性和价值，近来加密货币已成为最重要的货币之一。在市场上可用的其他加密货币中，比特币加密货币是最有价值和最著名的货币。许多人也在互联网和社交媒体平台上讨论有关比特币的问题。这些讨论有助于根据用户有关比特币的讨论来确定比特币的重要性，并可以帮助人们根据对该主题的观点来确定比特币的价值。在本文中，已经对用户关于比特币主题的推文进行了情感分析。为此，使用了真实世界的比特币Twitter数据集。数据集已分为五个单独的部分，以进行更好的比较分析，包括有关推文，转发，带有提及的推文，包含外部链接的推文以及讨论比特币加密货币的用户的全面广泛数据分析。在朴素贝叶斯情感分类算法的基础上，提出了一种情感分析框架，该算法被广泛用作文本数据的较好选择。所提出的框架能够对推文数据进行情感分析。结果类似于用户对比特币的高度肯定，而大多数人则表示对比特币的积极情绪。使用标准性能评估方法评估结果，包括精度，召回率，f1得分和准确性。

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