Alamoodi A H, Zaidan B B, Zaidan A A, Albahri O S, Mohammed K I, Malik R Q, Almahdi E M, Chyad M A, Tareq Z, Albahri A S, Hameed Hamsa, Alaa Musaab
Department of Computing, Sultan Idris University of Education (UPSI), Tanjong Malim, Malaysia.
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC.
Expert Syst Appl. 2021 Apr 1;167:114155. doi: 10.1016/j.eswa.2020.114155. Epub 2020 Oct 28.
The pandemic caused by the novel coronavirus occurred unexpectedly in China in December 2019. Tens of millions of confirmed cases and more than hundreds of thousands of confirmed deaths are reported worldwide according to the World Health Organisation. News about the virus is spreading all over social media websites. Consequently, these social media outlets are experiencing and presenting different views, opinions and emotions during various outbreak-related incidents. For computer scientists and researchers, big data are valuable assets for understanding people's sentiments regarding current events, especially those related to the pandemic. Therefore, analysing these sentiments will yield remarkable findings. To the best of our knowledge, previous related studies have focused on one kind of infectious disease. No previous study has examined multiple diseases via sentiment analysis. Accordingly, this research aimed to review and analyse articles about the occurrence of different types of infectious diseases, such as epidemics, pandemics, viruses or outbreaks, during the last 10 years, understand the application of sentiment analysis and obtain the most important literature findings. Articles on related topics were systematically searched in five major databases, namely, ScienceDirect, PubMed, Web of Science, IEEE Xplore and Scopus, from 1 January 2010 to 30 June 2020. These indices were considered sufficiently extensive and reliable to cover our scope of the literature. Articles were selected based on our inclusion and exclusion criteria for the systematic review, with a total of articles selected. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature in accordance with four main categories: lexicon-based models, machine learning-based models, hybrid-based models and individuals. The obtained articles were categorised into motivations related to disease mitigation, data analysis and challenges faced by researchers with respect to data, social media platforms and community. Other aspects, such as the protocol being followed by the systematic review and demographic statistics of the literature distribution, were included in the review. Interesting patterns were observed in the literature, and the identified articles were grouped accordingly. This study emphasised the current standpoint and opportunities for research in this area and promoted additional efforts towards the understanding of this research field.
新型冠状病毒引发的大流行于2019年12月在中国意外爆发。据世界卫生组织报告，全球有数千万确诊病例和超过数十万确诊死亡病例。关于该病毒的新闻在所有社交媒体网站上传播。因此，这些社交媒体平台在各种与疫情相关的事件中呈现出不同的观点、意见和情绪。对于计算机科学家和研究人员来说，大数据是理解人们对当前事件，特别是与大流行相关事件的情绪的宝贵资产。因此，分析这些情绪将产生显著的发现。据我们所知，以前的相关研究都集中在一种传染病上。以前没有研究通过情绪分析来研究多种疾病。因此，本研究旨在回顾和分析过去10年中关于不同类型传染病（如流行病、大流行、病毒或疫情爆发）发生情况的文章，了解情绪分析的应用，并获得最重要的文献发现。从2010年1月1日至2020年6月30日，在五个主要数据库（即ScienceDirect、PubMed、Web of Science、IEEE Xplore和Scopus）中系统搜索了相关主题的文章。这些索引被认为足够广泛和可靠，能够涵盖我们的文献范围。根据系统评价的纳入和排除标准选择文章，共选择了[X]篇文章。所有这些文章形成了一个连贯的分类法，按照四个主要类别描述文献中的相应当前观点：基于词典的模型、基于机器学习的模型、基于混合的模型和个体。所获得的文章被分类为与疾病缓解、数据分析以及研究人员在数据、社交媒体平台和社区方面面临的挑战相关的动机。系统评价所遵循的方案以及文献分布中的人口统计数据等其他方面也包括在评价中。在文献中观察到了有趣的模式，并据此对所识别的文章进行了分组。本研究强调了该领域当前的观点和研究机会，并促进了对这一研究领域的进一步理解。
