README.md

sensical

Public sense-making research for COVID-19 using Tweets.

setup

Pull down 'COVID-19-TweetIDs' submodule. Install dependencies. Configure 'twarc' with Twitter app consumer IDs. Documentation here. Hydrate tweets by running './hydrate.py'.

approach / todo

• The tweets/2020-05 and tweets/2020-04 folders has been zipped to save space on the device.
• We'll want to upload this data via the omic CLI so it shows up in OS.

wheel

Source paper: https://www.tandfonline.com/doi/abs/10.1080/10447318.2018.1427830?journalCode=hihc20.

sentiment/emotion

• https://github.com/bentrevett/pytorch-sentiment-analysis
• https://devblogs.microsoft.com/cse/2015/11/29/emotion-detection-and-recognition-from-text-using-deep-learning/
• https://github.com/ayushoriginal/Sentiment-Analysis-Twitter
• https://github.com/abdulfatir/twitter-sentiment-analysis
• https://github.com/vnnsrk/Twitter-Sentiment-Analysis
• https://github.com/P7h/Spark-MLlib-Twitter-Sentiment-Analysis
• https://github.com/riccardotommasini/twitter-sentiment-analysis
• https://github.com/marrrcin/ml-twitter-sentiment-analysis
• https://github.com/sharmaroshan/Twitter-Sentiment-Analysis
• https://github.com/kb22/Twitter-Sentiment-Analysis

scraping

datasets

• https://github.com/echen102/COVID-19-TweetIDs (about 85M tweets)
• https://github.com/thepanacealab/covid19_twitter (about 309M tweets)
• https://ieee-dataport.org/open-access/coronavirus-covid-19-geo-tagged-tweets-dataset

methods

• https://arxiv.org/pdf/2004.13932.pdf
• https://arxiv.org/pdf/2003.07372.pdf
• https://arxiv.org/pdf/2003.07602.pdf
• https://usc-melady.github.io/COVID-19-Tweet-Analysis/

etc.

• https://github.com/nytimes/covid-19-data
• https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data
• https://douroucouli.wordpress.com/2020/04/06/building-a-covid-19-knowledge-graph/
• https://towardsdatascience.com/covid-19-outbreak-tweet-analysis-on-face-masks-27ef5db199dd
• https://github.com/shirosaidev/stocksight