{"id":4298,"date":"2021-08-22T11:37:22","date_gmt":"2021-08-22T15:37:22","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?p=4298"},"modified":"2021-08-22T11:37:23","modified_gmt":"2021-08-22T15:37:23","slug":"4298","status":"publish","type":"post","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/2021\/08\/22\/4298\/","title":{"rendered":""},"content":{"rendered":"\n

Pitch 1<\/strong><\/p>\n\n\n\n

Social media sentiments to predict mental health of people during the stages of pandemic in the United States<\/strong><\/p>\n\n\n\n

  1. I think it is important to know how people are feeling, whether they are hopeful, devastated, excited, or feeling fine during the pandemic.<\/li>
  2. With new variants showing up, people\u2019s sentiments may keep changing. So, I want to see if virus variants, mask mandates loosening up, or vaccine rollout had an impact on people’s sentiments. For example, people were probably being hopeful and excited after getting vaccinated but the delta variant may have increased the negative sentiments.<\/li>
  3. I plan to use Twitter data with a sentiment analysis machining learning model and then visualizing the data with interactive charts<\/li>
  4. I will be using Twitter\u2019s API code and collect the tweets with #covid19, #pandemic, and other covid related hashtags (#).<\/li>
  5. Timeline of the covid-19 event (From the timeline below I will only pick certain events): https:\/\/www.ajmc.com\/view\/a-timeline-of-covid19-developments-in-2020<\/a><\/li>
  6. Resources: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3572023. <\/a> <\/li><\/ol>\n\n\n\n

    Pitch 2<\/strong><\/p>\n\n\n\n

    Social media sentiments to predict the covid situation and vaccine rollout in different countries<\/strong><\/p>\n\n\n\n

    1. Why is this important?