AI-based Automated Extraction of Location-Oriented COVID-19 Sentiments
作者机构：Federal GovernmentMelbourneVIC3000Australia Information Systems DepartmentUmm Al-Qura University(UQU)Makkah24382Saudi Arabia
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2022年第72卷第8期
页 面：3631-3649页
核心收录：
学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Entity recognition AI-based social media monitoring sentiment analysis decision support system COVID-19
摘 要：The coronavirus disease(COVID-19)pandemic has affected the lives of social media users in an *** are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of ***,understanding location-oriented sentiments about this situation is of prime importance for political leaders,and strategic *** this end,we present a new fully automated algorithm based on artificial intelligence(AI),for extraction of location-oriented public sentiments on the COVID-19 *** designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation,sentiment analysis,location entity detection,and decomposition tree *** deployed fully automated algorithm on live Twitter feed from July 15,2021 and it is still running as of 12 January,*** system was evaluated on a limited dataset between July 15,2021 to August 10,*** this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location *** total,13,220 location entities were detected during the evaluation period,and the rates of average precision and recall rate were 0.901 and 0.967,*** of 12 January,2022,the proposed solution has detected 43,169 locations using entity *** to the best of our knowledge,this study is the first to report location intelligence with entity detection,sentiment analysis,and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.
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