基于情绪分析的比特币价格预测和NFT生成器

IF 0.3
Mitali Lade, Rashmi Welekar, Charanjeet Dadiyala
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引用次数: 0

摘要

人们发现,推特情绪在预测比特币价格是涨是跌、是攀升还是下跌方面很有用。对比特币生态系统中的市场活动和情绪进行建模,可以洞察比特币的价格预测。我们不仅考虑了从推特中获取的情感,还考虑了推特的数量。为了优化时间窗口,使表达的情绪成为价格变化的可靠预测因素,我们提供了来自研究的数据,这些研究在不同的时间粒度上检查了情绪和未来价格之间的联系。我们在本研究中证明,不仅可以预测价格方向,而且可以预测价格变动的幅度,这是本研究的主要科学贡献。近年来,不可替代代币(NFT)作为一种基于区块链的应用获得了国际关注。可以存储在许多区块链上的最普遍的NFT类型是数字艺术。我们对CryptoPunks (NFT市场上最受欢迎的收藏)进行了研究,以检查和描述每一个主要的道德挑战。我们从三个角度调查了道德问题:设计、贸易交易和相关的Twitter话题。使用Python库、Twitter爬虫和情绪分析工具,我们从Twitter上抓取数据,并对比特币和nft进行分析和预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bitcoin Price Prediction and NFT Generator Based on Sentiment Analysis
Twitter sentiment has been found to be useful in predicting whether the price of Bitcoin will rise or fall will climb or decline. Modelling market activity and hence emotion in the Bitcoin ecosystem gives insight into Bitcoin price forecasts. We take into account not just the emotion retrieved not just from tweets, but also from the quantity of tweets. With the goal of optimising time window within which expressed emotion becomes a credible predictor of price change, we provide data from research that examined the link among both sentiment and future price at various temporal granularities. We demonstrate in this study that not only can price direction be anticipated, but also the magnitude of price movement with same accuracy, and this is the study's major scientific contribution. Non-Fungible Token (NFT) has gained international interest in recent years as a blockchain-based application. The most prevalent kind of NFT that can be stored on many blockchains is digital art. We did studies on CryptoPunks, the most popular collection on the NFT market, in examine and depict each and every major ethical challenges. We investigated ethical concerns from three perspectives: design, trade transactions, and relevant Twitter topics. Using Python libraries, a Twitter crawler, and sentiment analysis tools, we scraped data from Twitter and performed the analysis and prediction on bitcoin and NFTs.
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来源期刊
International Journal of Next-Generation Computing
International Journal of Next-Generation Computing COMPUTER SCIENCE, THEORY & METHODS-
自引率
66.70%
发文量
60
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