Abstract
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.
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CITATION STYLE
Gottipati, S., Shim, K. J., Teo, H. H., Nityan, K., & Shivam, S. (2021). Analyzing Tweets on New Norm: Work from Home during COVID-19 Outbreak. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021 (pp. 500–507). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CCWC51732.2021.9375936
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