基于机器学习和Vader Lexicon方法的Covid-19疫苗推文情绪分析

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Vishakha Arya, A. Mishra, Alfonso González-Briones
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引用次数: 0

摘要

2019年新型冠状病毒病(COVID-19)随后被命名为严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2),折磨着全世界数百万人的生活。有效和安全的疫苗接种可能会遏制大流行。本研究旨在应用VADER词典、TextBlob和机器学习方法:分析和检测在2019冠状病毒病大流行期间Twitter上的持续情绪,了解全球公众对疫苗的反应以及对疫苗有效性的担忧。从2020年8月18日至2021年7月20日,使用# covid - vaccine #Vaccines #冠状病毒疫苗标签检索了20多万条与疫苗相关的推文。数据分析采用VADER词典法预测情感极性、计数和情感分布,TextBlob法确定主观性和极性,并与随机森林(Random Forest, RF)和Logistic回归(Logistic Regression, LR)等两种模型进行比较。结果决定了公众对疫苗的态度是积极的,其次是中立和消极的。机器学习分类模型在疫苗推文上的表现优于VADER词典方法。预计这项研究的目的是帮助政府制定长期政策,并为正在遭受负面思想的人们提供更好的环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiments Analysis of Covid-19 Vaccine Tweets Using Machine Learning and Vader Lexicon Method
The novel Coronavirus disease of 2019 (COVID-19) has subsequently named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have tormented the lives of millions of people worldwide. Effective and safe vaccination might curtail the pandemic. This study aims to apply the VADER lexicon, TextBlob and machine learning approach: to analyze and detect the ongoing sentiments during the affliction of the Covid-19 pandemic on Twitter, to understand public reaction worldwide towards vaccine and concerns about the effectiveness of the vaccine. Over 200000 tweets vaccine-related using hashtags #CovidVaccine #Vaccines #CornavirusVaccine were retrieved from 18 August 2020 to 20 July 2021. Data analysis conducted by VADER lexicon method to predict sentiments polarity, counts and sentiment distribution, TextBlob to determine the subjectivity and polarity, and also compared with two other models such as Random Forest (RF) and Logistic Regression (LR). The results determine sentiments that public have a positive stance towards a vaccine follows by neutral and negative. Machine learning classification models performed better than the VADER lexicon method on vaccine Tweets. It is anticipated this study aims to help the government in long run, to make policies and a better environment for people suffering from negative thoughts during the ongoing pandemic.
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
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