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分析 COVID-19 对加密货币和金融市场的影响,并使用深度集成模型进行情绪分析。

Analyzing influence of COVID-19 on crypto & financial markets and sentiment analysis using deep ensemble model.

机构信息

Division of Business Administration and Economics, Morehouse College, Atlanta, GA, United Sates of America.

Department of Electrical Engineering, University of North Texas, Denton, TX, United States of America.

出版信息

PLoS One. 2023 Sep 28;18(9):e0286541. doi: 10.1371/journal.pone.0286541. eCollection 2023.

Abstract

COVID-19 affected the world's economy severely and increased the inflation rate in both developed and developing countries. COVID-19 also affected the financial markets and crypto markets significantly, however, some crypto markets flourished and touched their peak during the pandemic era. This study performs an analysis of the impact of COVID-19 on public opinion and sentiments regarding the financial markets and crypto markets. It conducts sentiment analysis on tweets related to financial markets and crypto markets posted during COVID-19 peak days. Using sentiment analysis, it investigates the people's sentiments regarding investment in these markets during COVID-19. In addition, damage analysis in terms of market value is also carried out along with the worse time for financial and crypto markets. For analysis, the data is extracted from Twitter using the SNSscraper library. This study proposes a hybrid model called CNN-LSTM (convolutional neural network-long short-term memory model) for sentiment classification. CNN-LSTM outperforms with 0.89, and 0.92 F1 Scores for crypto and financial markets, respectively. Moreover, topic extraction from the tweets is also performed along with the sentiments related to each topic.

摘要

新冠疫情严重影响了全球经济,导致发达国家和发展中国家的通胀率都有所上升。新冠疫情还对金融市场和加密货币市场产生了重大影响,但一些加密货币市场在疫情期间蓬勃发展并达到了峰值。本研究分析了新冠疫情对公众对金融市场和加密货币市场的看法和情绪的影响。它对新冠疫情高峰期发布的与金融市场和加密货币市场相关的推文进行了情感分析。通过情感分析,研究人员调查了人们在新冠疫情期间对这些市场投资的情绪。此外,还进行了市场价值方面的损害分析,以及金融和加密货币市场的最坏时期。为了进行分析,数据是使用 SNSscraper 库从 Twitter 上提取的。本研究提出了一种称为 CNN-LSTM(卷积神经网络-长短时记忆模型)的混合模型,用于情感分类。CNN-LSTM 的表现优于 0.89 和 0.92 的加密货币和金融市场 F1 分数。此外,还从推文中提取了主题,并与每个主题相关的情绪一起进行了分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/622c/10538772/b71bd25149b1/pone.0286541.g001.jpg

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