Analyzing tweets on new norm: Work from home during COVID-19 outbreak

• Main Authors: GOTTIPATI, Swapna, SHIM, Kyong Jin, TEO, Hui Hian, NITYANAND, Karthik, SHIVAM, Shreyansh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: work-from-home; COVID-19; data analytics; NLP; social media; Databases and Information Systems; Data Science; Numerical Analysis and Scientific Computing; Public Health; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/6027; https://ink.library.smu.edu.sg/context/sis_research/article/7030/viewcontent/Analyzing_tweets_on_New_norm_Work_from_home_during_COVID_19_av.pdf
• Institution: Singapore Management University

The COVID-19 pandemic triggered a large-scale work-from-home trend globally in recent months. In this paper, we study the phenomenon of “work-from-home” (WFH) by performing social listening. We propose an analytics pipeline designed to crawl social media data and perform text mining analyzes on textual data from tweets scrapped based on hashtags related to WFH in COVID-19 situation. We apply text mining and NLP techniques to analyze the tweets for extracting the WFH themes and sentiments (positive and negative). Our Twitter theme analysis adds further value by summarizing the common key topics, allowing employers to gain more insights on areas of employee concerns due to pandemic.