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    SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic

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    Type
    Preprint
    Authors
    Yang, Qiang
    Alamro, Hind cc
    Albaradei, Somayah
    Salhi, Adil
    Lv, Xiaoting
    Ma, Changsheng
    Alshehri, Manal
    Jaber, Inji Ibrahim
    Tifratene, Faroug
    Wang, Wei
    Gojobori, Takashi cc
    Duarte, Carlos M. cc
    Gao, Xin cc
    Zhang, Xiangliang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division and Computational Biosciences Research Center (CBRC), King Abdullah University of Science and Technology (KAUST).
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computational Bioscience Research Center (CBRC)
    Computer Science
    Business Operations
    Bioscience Program
    Biological and Environmental Sciences and Engineering (BESE) Division
    Marine Science Program
    Red Sea Research Center (RSRC)
    Date
    2020-06-18
    Permanent link to this record
    http://hdl.handle.net/10754/666195
    
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    Abstract
    Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories. As of June 18, 2020, in total, there are now over 8,400,000 cases and over 450,000 related deaths. This causes massive losses in the economy and jobs globally and confining about 58% of the global population. In this paper, we introduce SenWave, a novel sentimental analysis work using 105+ million collected tweets and Weibo messages to evaluate the global rise and falls of sentiments during the COVID-19 pandemic. To make a fine-grained analysis on the feeling when we face this global health crisis, we annotate 10K tweets in English and 10K tweets in Arabic in 10 categories, including optimistic, thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, official report, and joking. We then utilize an integrated transformer framework, called simpletransformer, to conduct multi-label sentimental classification by fine-tuning the pre-trained language model on the labeled data. Meanwhile, in order for a more complete analysis, we also translate the annotated English tweets into different languages (Spanish, Italian, and French) to generated training data for building sentiment analysis models for these languages. SenWave thus reveals the sentiment of global conversation in six different languages on COVID-19 (covering English, Spanish, French, Italian, Arabic and Chinese), followed the spread of the epidemic. The conversation showed a remarkably similar pattern of rapid rise and slow decline over time across all nations, as well as on special topics like the herd immunity strategies, to which the global conversation reacts strongly negatively. Overall, SenWave shows that optimistic and positive sentiments increased over time, foretelling a desire to seek, together, a reset for an improved COVID-19 world.
    Publisher
    arXiv
    arXiv
    2006.10842
    Additional Links
    https://arxiv.org/pdf/2006.10842
    Collections
    Biological and Environmental Sciences and Engineering (BESE) Division; Red Sea Research Center (RSRC); Preprints; Bioscience Program; Marine Science Program; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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