Nasir Adeena, Shah Munam Ali, Ashraf Ummarah, Khan Abid, Jeon Gwanggil
Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, Pakistan.
School of Computing, Engineering & Digital Technologies, Teesside University, Tees Valley, TS1 3BX, Middlesbrough, UK.
Comput Electr Eng. 2021 Dec;96:107526. doi: 10.1016/j.compeleceng.2021.107526. Epub 2021 Oct 11.
The outbreak of novel coronavirus (COVID-19) has extremely shaken the whole world. COVID-19 has increased human distress, damaged the global economy, flipped the lives of many people around the world upside down, and has had a huge effect on the health, economic, environmental, and social sectors. This study aims to determine the social and economic trends in the outbreak of COVID-19 in Pakistan. Machine learning techniques learn patterns from historical data and make predictions on its basis. Furthermore, an online survey has been conducted to collect data and a total of 410 responses are collected. Machine learning techniques have been used to highlight the impact of COVID-19 on daily life. Moreover, sentiment analysis on tweets of Pakistan has also been performed to evaluate the positive and negative sentiments of the people on COVID-19.
新型冠状病毒(COVID-19)的爆发极大地震动了整个世界。COVID-19加剧了人类的苦难,损害了全球经济,颠覆了世界各地许多人的生活,并对健康、经济、环境和社会部门产生了巨大影响。本研究旨在确定巴基斯坦COVID-19疫情中的社会和经济趋势。机器学习技术从历史数据中学习模式并在此基础上进行预测。此外,还进行了一项在线调查以收集数据,共收集到410份回复。机器学习技术已被用于突出COVID-19对日常生活的影响。此外,还对巴基斯坦的推文进行了情感分析,以评估人们对COVID-19的积极和消极情绪。