Since the outbreak of Covid-19, the U.S. Congress has introduced and processed a series of bills responding to the pandemic. Our project aims to explore the changing patterns of the keywords in the text of legislative bills introduced in the Congress during 2020, and see if there exists any correlations between these keyword patterns and the trend in COVID-19 cases and economic indicators in the U.S. on a quarterly basis.
PART1-Data_Scraping-Nikki_Ting: contains the code for scraping the U.S. Congress webpage for bills related to the COVID-19 pandemic and the output
PART2-Text_Analysis_Keywords-Naiyu_Jiang: contains the data, code, and results of the text pre-processing and keywords extraction part.
PART3-Correlation_Matrix_Heatmap-Zheng_He: contains the code for creating the final dataframe which includes the keywords whose frequencies are within the top-20 of all quarterly keyword lists produced by Naiyu Jiang's code, the correlation matrix based upon the final dataframe, and the heatmap of that correlation matrix.
Presentation: Contains our presentation slides.