With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM).

随着 COVID-19 病例的增加，每个国家都面临着一种奇怪的压力情况，要求他们做出安排以控制人口并适当利用可用资源。全球阳性病例的迅速增加在人们中造成了恐慌、焦虑和抑郁。发现这种致命疾病的影响与人群的身心健康成正比。截至 2020 年 10 月 28 日，超过 4000 万人检测呈阳性，超过 100 万人死亡。在此期间扰乱人类生活的最主要工具是社交媒体。有关 COVID-19 的推文，无论是一些阳性病例还是死亡病例，都在世界不同地区的人们中引发了一波恐惧和焦虑。没有人可以否认社交媒体无处不在，每个人都直接或间接地与之相关的事实。这为研究人员和数据科学家提供了访问数据以供学术和研究使用的机会。社交媒体数据包含许多与 COVID-19 等现实事件相关的数据。在本文中，通过 R 编程语言对 Twitter 数据进行了分析。我们根据标签关键字收集了 Twitter 数据，包括 COVID-19、冠状病毒、死亡、新病例、康复。在这项研究中，我们设计了一种称为混合异构支持向量机 (H-SVM) 的算法，并执行情绪分类并将它们分类为正面、负面和中性的情绪分数。我们还比较了所提出算法在某些参数上的性能，例如精度，