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Content and Sentiment Analysis of The New York Times Coronavirus (2019-nCOV) Articles with Natural Language Processing (NLP) and Leximancer
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References

  • 2020Effects of COVID-19 on business and research2,021 cited
  • 2020Aging in Times of the COVID-19 Pandemic: Avoiding Ageism and Fostering Intergenerational Solidarity593 cited
  • 2014New Directions in Agenda-Setting Theory and Research470 cited
  • 2020Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter331 cited
  • 2010Mandatory Influenza Vaccination of Health Care Workers: Translating Policy to Practice289 cited
  • 2021COVID-19, consumer behavior, technology, and society: A literature review and bibliometric analysis280 cited
  • 2020Exposure to coronavirus news on mainstream media: The role of risk perceptions and depression219 cited
  • 2021Does a Crisis Change News Habits? A Comparative Study of the Effects of COVID-19 on News Media Use in 17 European Countries215 cited
  • 2020A content analysis of Chinese news coverage on COVID-19 and tourism214 cited
  • 2013Making sense of big text: a visual-first approach for analysing text data using Leximancer and Discursis199 cited
  • 2020What Went Wrong: Corona and the World after the Full Stop179 cited
  • 2020COVID-19 Lockdown Restrictions and Online Media Consumption in Germany168 cited
  • 2020Effects of COVID-19 on the Italian agri-food supply and value chains161 cited
  • 2020Using Social Media to Our Advantage: Alleviating Anxiety During a Pandemic

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153 cited
  • 2019Tackling social media data analysis134 cited
  • 2020Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning120 cited
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