Starting in December 2019, the entire world has been suffering from COVID 19 pandemic. During this pandemic, a lot of scientific research and articles were published. Apart from research, there were a lot of social media posts published by the general public.
1. Collect Twitter messages regarding COVID 19. You can use hashtags such as #COVID19, #Corona or #CoronaVirus. Or you can use available data sets. It should have at least 2,000 Twitter messages and each message should have more than 150 characters.
The sample data set can be found at https://ieee-dataport.org/open-access/corona-virus-covid-19-tweets-dataset
2. Identify the distribution of tweets in the world map.
3. Identify the tweet counts for different languages such as English, Spanish, French, etc.
4. Is there a relationship between the number of COVID 19 confirmed cases or the number of deaths due to COVID 19? Explain your findings in detail. You can use https://coronavirus.jhu.edu/map.html to obtain the latest results.
5. Generate the word cloud for the above data set after applying proper data cleaning techniques and comment on the results
6. By means of the association technique, find what commonly used words are. Specify the association technique and it is parameters.
7. How you can improve the above results? Explain with web mining techniques.
@Malinda Wickramasinghe Hello, some of the steps you outlined can be done with Tableau, but could you clarify if you expect the output to be a Tableau workbook or something else?
Regards,
Tatyana
