Machine learning and Lexical Semantic-based Sentiment Analysis for Determining the Impacts of the COVID-19 Vaccine

Samrat Alam, Sajal Das Shovon, Naimul Hoque Joy
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Abstract

In 2020 COVID-19 has taken the world by storm. Scientists from around the world are still working to develop a more effective vaccine for this disease. AstraZeneca, Moderna, Sputnik V and Comirnaty (Pfizer) are just a few of the vaccines that have been developed and are now being used by large populations. Social media is a powerful tool for people to express their opinions on current events, such as COVID-19 and its vaccine. It is highly noticeable that people are becoming increasingly concerned about the availability and effectiveness of these vaccines and other remedies for COVID-19. Healthcare organizations and professionals can acquire useful insights into vaccination safety by evaluating people’s sentiments. Furthermore, it can also assist to prevent unnecessary panic and the spread of misinformation among people. In this paper, a comprehensive analysis of people’s sentiments regarding the vaccination against COVID-19 is shown. Twitter’s data regarding the vaccine for COVID-19 from January to December of 2020 was collected from Kaggle for analysis. Necessary preprocessing techniques have been used to prepare and label the data based on textual sentiment using the lexical semantic methods: TextBlob and VADER. Various machine learning methods like Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), merged model (RNN+CNN) and Logistic Regression have been used to analyze the public sentiments and to visualize their concerns regarding the vaccination against COVID-19 throughout 2020. Then, the results from both TextBlob and VADER were compared in order to obtain the highest possible accuracy and to better understand the reasons for them.
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确定COVID-19疫苗影响的机器学习和基于词汇语义的情感分析
2020年,2019冠状病毒病席卷全球。来自世界各地的科学家仍在努力开发针对这种疾病的更有效的疫苗。阿斯利康(AstraZeneca)、Moderna、Sputnik V和Comirnaty(辉瑞)只是已经开发并正在大量人群中使用的疫苗中的一小部分。社交媒体是人们表达对COVID-19及其疫苗等时事观点的有力工具。非常值得注意的是,人们越来越关注这些疫苗和其他COVID-19补救措施的可得性和有效性。医疗机构和专业人员可以通过评估人们的情绪来获得有关疫苗接种安全性的有用见解。此外,它还有助于防止不必要的恐慌和错误信息在人们之间的传播。本文全面分析了民众对COVID-19疫苗接种的看法。Twitter从2020年1月至12月收集了有关COVID-19疫苗的数据,并从Kaggle进行了分析。使用了必要的预处理技术,利用词汇语义方法TextBlob和VADER对基于文本情感的数据进行准备和标记。各种机器学习方法,如循环神经网络(RNN)、卷积神经网络(CNN)、合并模型(RNN+CNN)和逻辑回归,已被用于分析公众情绪,并可视化他们对2020年全年COVID-19疫苗接种的担忧。然后,比较TextBlob和VADER的结果,以获得尽可能高的准确性,并更好地理解其原因。
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