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

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

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 text...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
Description
Summary: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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