covid19-socialmedia-datamining

Publication: Modeling spatiotemporal pattern of depressive symptoms caused by COVID-19 using social media data mining

Name		Name	Last commit message	Last commit date
Latest commit History 20 Commits
.vscode		.vscode
depression		depression
mgwr		mgwr
tfmodel		tfmodel
twitter-demographics		twitter-demographics
us-census		us-census
utils		utils
.gitignore		.gitignore
LICENSE		LICENSE
Model-Review-1.ipynb		Model-Review-1.ipynb
README.md		README.md
activate-jupyter.sh		activate-jupyter.sh
clean-twitter-all-sample.ipynb		clean-twitter-all-sample.ipynb
clean-twitter-all.ipynb		clean-twitter-all.ipynb
clean-twitter-all.py		clean-twitter-all.py
config.py		config.py
cursor-config.json		cursor-config.json
cursor-twitter-with-tag.ipynb		cursor-twitter-with-tag.ipynb
cursor-twitter.py		cursor-twitter.py
filter.json		filter.json
nohup-exec.sh		nohup-exec.sh
requirements.txt		requirements.txt
sentiment-analysis.ipynb		sentiment-analysis.ipynb
stream-twitter-with-tag.ipynb		stream-twitter-with-tag.ipynb
test-jupyter.ipynb		test-jupyter.ipynb
testdump.json		testdump.json
time_series_covid19_confirmed_US.csv		time_series_covid19_confirmed_US.csv
time_series_covid19_deaths_US.csv		time_series_covid19_deaths_US.csv
twitter-1hour-plot.ipynb		twitter-1hour-plot.ipynb
twitter-covid-fear.csv		twitter-covid-fear.csv
upload-to-google-drive.py		upload-to-google-drive.py

dyllanwli/covid19-socialmedia-datamining