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Pydata Taipei 2020

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Is a Twitter user's location correlated with their opinion on #COVID-19?
Presented by CML's student: Anna Chang

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Pydata Taipei 2020

  1. 1. Is a Twitter user’s location correlated with their opinion on #COVID-19? Rong-Ching Chang Chun-Ming Lai, Assist Prof Information Security Lab X Social Computing And Information Security Lab (SCIS) Tunghai University Twitter@AnnCC12
  2. 2. Agenda • General introduction of How you get data from Twitter • Data, Data Pre-processing • Mythology • What top 5 countries of English users tweets about during Feb to May • How is it different from getting data from Facebook?
  3. 3. How do you get data from Twitter Twitter API Hydration Web Scrapping Tools
  4. 4. Twitter API Twitter Account Twitter Developer Account Create Application
  5. 5. Open data & Covid & Hydration Citation Dataset from: Umair Qazi, Muhammad Imran, Ferda Ofli. GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information. ACM SIGSPATIAL Special, May 2020. doi: https://doi.org/10.1145/3404820.3404823
  6. 6. Basic Data Pre-processing A -> a @ mail # hashtags https:// @mention Dooing -> doing Doing - > do :$%#@ n, nan stopwords Filter lang Filter geography Remove top 20, tail 20 words Tokenization Word frequency WorldCloud, Trigram
  7. 7. Mythology Word Cloud Sentiment Analysis- Polarity, Subjective N Gram
  8. 8. Word Cloud Word Cloud Word Frequency Categorization Mixed A word cloud is a kind of weighted list to visualize language or text data the size of font indicates the number of subcategories of a collection. the size of font represents the number of keywords that appears in the collection. Cite:Yuping Jin, “Development of Word Cloud Generator Software Based on Python”, GCMM 2016
  9. 9. N Gram ‘soon’ ‘soon’, ‘Comprehensive’ ‘soon’, ‘Comprehensive’, ‘testing’ 2- Gram Bi-Gram 1- Gram -Gram 3- Gram Tri-Gram
  10. 10. N Gram ‘released’ ‘charles’ ‘released’, ‘melbourne’ ‘charles’,’test’ ‘released’, ‘melbourne’, ‘immigration’ ‘charles’,’test’,’positive’
  11. 11. Sentiment Analysis- Polarity Negative -1.0 Positive 1.0 Neutral 0.0
  12. 12. Sentiment Analysis- Subjective Subjective 1.0 Objective 0.0
  13. 13. Sentiment Analysis-Polarity, Subjective
  14. 14. Top 5 Countries (ISO 3166-1 alpha-2) • 'us’ United States of America • ‘in’ India • ‘gb’ United Kingdom • ‘cn’ China • 'au’ Australia • Date • 2/1 • 3/16 • 3/25 • 4/25
  15. 15. Top 5 Countries Total Tweets
  16. 16. Top 5 Countries Polarity & Subjective
  17. 17. United States 2020/02/01 2020/03/16 2020/03/25 2020/04/25
  18. 18. United States Baltimore protests Freddie Gray's death Odion Jude Ighalo a footballer who played in china
  19. 19. India 2020/03/16 2020/03/25 2020/04/25
  20. 20. India
  21. 21. United Kingdom 2020/03/16 2020/03/25 2020/04/25
  22. 22. United Kingdom
  23. 23. China 2020/03/16 2020/03/25 2020/04/25
  24. 24. China
  25. 25. Australia 2020/03/16 2020/03/25 2020/04/25
  26. 26. Australia
  27. 27. How do you get data from Facebook A tool from Facebook to help follow, analyze, and report on what’s happening across social media.
  28. 28. How can you access it? Chrome Extension University researchers
  29. 29. Crowd Tangle Chrome Extension
  30. 30. Sentiment Analysis-Polarity, Subjective
  31. 31. Thank you Rong-Ching Chang Chun-Ming Lai, Assist Prof Information Security Lab X Social Computing And Information Security Lab (SCIS) Tunghai University Twitter@AnnCC12

Is a Twitter user's location correlated with their opinion on #COVID-19? Presented by CML's student: Anna Chang

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