Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19  

作　　者：Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari Mazin Abed Mohammed Marwan Ali Albahar Mohammed Thanoon Amena Mahmoud 

机构地区：[1]Department of Software Engineering,University of Management and Technology,Lahore,54770,Pakistan [2]School of Computer Science,The University of Sydney,Sydney,Australia [3]Department of Computer Science,College of Science,Nawroz University,Duhok,42001,Kurdistan Region,Iraq [4]College of Computer Science and Information Technology,University of Anbar,Anbar,31001,Iraq [5]Department of Computer Science,Umm Al Qura University,Mecca,24211,Saudi Arabia [6]Computer Science Department,Faculty of Computers and Information,Kafrelsheikh University,Kafrelsheikh,Egypt

出　　处：《Computer Systems Science & Engineering》2023年第10期791-807,共17页计算机系统科学与工程（英文）

基　　金：The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4400257DSR01).

摘　　要：Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern era.Twitter,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment analysis.In recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets.Opinions or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing opinions.The process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods.The deep learning-based model long-short-term memory(LSTM)performed better than machine learning approaches.Long short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

关 键 词：COVID-19 artificial intelligence machine learning deep learning sentimental analysis support vector classifier 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R563.1[自动化与计算机技术—控制科学与工程]