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10.1016/j.asoc.2020.107057 http://scihub22266oqcxt.onion/10.1016/j.asoc.2020.107057 33519326!7832522!33519326     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33519326&cmd=llinks): Failed to open stream: HTTP request failed! 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Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA |pdf=|usr=}}{{33519326}} gab.com Text Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA JO: Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=33519326 #FOAvip Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. It is a challenge identifying interesting and useful content from large text-streams in different languages, few works have explored languages other than English. In this paper, we use topic identification and sentiment analysis to explore a large number of tweets in both countries with a high number of spreading and deaths by COVID-19, Brazil, and the USA. We employ 3,332,565 tweets in English and 3,155,277 tweets in Portuguese to compare and discuss the effectiveness of topic identification and sentiment analysis in both languages. We ranked ten topics and analyzed the content discussed on Twitter for four months providing an assessment of the discourse evolution over time. The topics we identified were representative of the news outlets during April and August in both countries. We contribute to the study of the Portuguese language, to the analysis of sentiment trends over a long period and their relation to announced news, and the comparison of the human behavior in two different geographical locations affected by this pandemic. It is important to understand public reactions, information dissemination and consensus building in all major forms, including social media in different countries. Twit Text FOAVip Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA ????Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=33519326 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33519326 #FOAvip English Wikipedia <ref>{{Cite pmid|33519326}}</ref> Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA #MMPMID33519326 Garcia K; Berton L Appl Soft Comput 2021[Mar]; 101 (ä): 107057 PMID33519326show ga Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. It is a challenge identifying interesting and useful content from large text-streams in different languages, few works have explored languages other than English. In this paper, we use topic identification and sentiment analysis to explore a large number of tweets in both countries with a high number of spreading and deaths by COVID-19, Brazil, and the USA. We employ 3,332,565 tweets in English and 3,155,277 tweets in Portuguese to compare and discuss the effectiveness of topic identification and sentiment analysis in both languages. We ranked ten topics and analyzed the content discussed on Twitter for four months providing an assessment of the discourse evolution over time. The topics we identified were representative of the news outlets during April and August in both countries. We contribute to the study of the Portuguese language, to the analysis of sentiment trends over a long period and their relation to announced news, and the comparison of the human behavior in two different geographical locations affected by this pandemic. It is important to understand public reactions, information dissemination and consensus building in all major forms, including social media in different countries. äTopic detection and sentiment analysis in Twitter content related to C(epmc).pdf DeepDyve Pubget Overpricing