Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

1-2021

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.

Keywords

work-from-home, COVID-19, data analytics, NLP, social media

Discipline

Databases and Information Systems | Data Science | Numerical Analysis and Scientific Computing | Public Health | Social Media

Research Areas

Data Science and Engineering

Publication

2021 11th IEEE Annual Computing and Communication Workshop and Conference: January 27-30, Virtual: Proceedings

First Page

500

Last Page

507

ISBN

9780738143941

Identifier

10.1109/CCWC51732.2021.9375936

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

7-8-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/CCWC51732.2021.9375936

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