{"title":"情绪、数量和案例:COVID-19大流行期间Twitter讨论分析","authors":"Zainab Tariq Soomro, Sardar Haider Waseem Ilyas, Ussama Yaqub","doi":"10.1109/BESC51023.2020.9348291","DOIUrl":null,"url":null,"abstract":"In this paper, we analyze over 18 million coronavirus related Twitter messages collected between March 1, 2020 and May 31, 2020. We perform sentiment analysis using VADER, a rule-based supervised machine learning model, to evaluate the relationship between public sentiment and number of COVID-19 cases. We also look at the frequency of mentions of a country in tweets and the rise in its' daily number of COVID-19 cases. Some of our findings include the discovery of a correlation between the number of tweets mentioning Italy, USA, and UK and the daily increase in new COVID-19 cases in these countries.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Sentiment, Count and Cases: Analysis of Twitter discussions during COVID-19 Pandemic\",\"authors\":\"Zainab Tariq Soomro, Sardar Haider Waseem Ilyas, Ussama Yaqub\",\"doi\":\"10.1109/BESC51023.2020.9348291\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we analyze over 18 million coronavirus related Twitter messages collected between March 1, 2020 and May 31, 2020. We perform sentiment analysis using VADER, a rule-based supervised machine learning model, to evaluate the relationship between public sentiment and number of COVID-19 cases. We also look at the frequency of mentions of a country in tweets and the rise in its' daily number of COVID-19 cases. Some of our findings include the discovery of a correlation between the number of tweets mentioning Italy, USA, and UK and the daily increase in new COVID-19 cases in these countries.\",\"PeriodicalId\":224502,\"journal\":{\"name\":\"2020 7th International Conference on Behavioural and Social Computing (BESC)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 7th International Conference on Behavioural and Social Computing (BESC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BESC51023.2020.9348291\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 7th International Conference on Behavioural and Social Computing (BESC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BESC51023.2020.9348291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sentiment, Count and Cases: Analysis of Twitter discussions during COVID-19 Pandemic
In this paper, we analyze over 18 million coronavirus related Twitter messages collected between March 1, 2020 and May 31, 2020. We perform sentiment analysis using VADER, a rule-based supervised machine learning model, to evaluate the relationship between public sentiment and number of COVID-19 cases. We also look at the frequency of mentions of a country in tweets and the rise in its' daily number of COVID-19 cases. Some of our findings include the discovery of a correlation between the number of tweets mentioning Italy, USA, and UK and the daily increase in new COVID-19 cases in these countries.
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