The document discusses the correlation between Twitter users' locations and their opinions on COVID-19, presenting methodologies for data acquisition from Twitter, data pre-processing, and sentiment analysis. It highlights the top five countries (US, India, UK, China, Australia) that tweeted about COVID-19 from February to May, along with sentiment polarity and subjectivity over time. It also briefly contrasts data collection from Twitter and Facebook and mentions tools for sentiment analysis.

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. 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. How do you get data from Twitter
Twitter API Hydration Web Scrapping Tools

4. Twitter API
Twitter Account Twitter Developer
Account
Create Application

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. 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. Mythology
Word Cloud Sentiment
Analysis-
Polarity,
Subjective
N Gram

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. N Gram
‘soon’
‘soon’, ‘Comprehensive’
‘soon’, ‘Comprehensive’, ‘testing’
2- Gram
Bi-Gram
1- Gram
-Gram
3- Gram
Tri-Gram

10. N Gram
‘released’
‘charles’
‘released’, ‘melbourne’
‘charles’,’test’
‘released’, ‘melbourne’, ‘immigration’
‘charles’,’test’,’positive’

11. Sentiment Analysis- Polarity
Negative
-1.0
Positive
1.0
Neutral
0.0

12. Sentiment Analysis- Subjective
Subjective
1.0
Objective
0.0

13. Sentiment Analysis-Polarity, Subjective

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. Top 5 Countries Total Tweets

16. Top 5 Countries Polarity & Subjective

17. United States
2020/02/01
2020/03/16 2020/03/25 2020/04/25

18. United States Baltimore protests Freddie Gray's death
Odion Jude Ighalo a footballer who played in china

19. India
2020/03/16 2020/03/25 2020/04/25

20. India

21. United Kingdom
2020/03/16 2020/03/25 2020/04/25

22. United Kingdom

23. China
2020/03/16 2020/03/25 2020/04/25

24. China

25. Australia
2020/03/16 2020/03/25 2020/04/25

26. Australia

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. How can you access it?
Chrome Extension University
researchers

29. Crowd Tangle Chrome Extension

30. Sentiment Analysis-Polarity, Subjective

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