Analyzing social media messages of public sector organizations utilizing sentiment analysis and topic modeling

Ussama Yaqub, Soon Ae Chun, Vijayalakshmi Atluri, Jaideep Vaidya

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we perform sentiment analysis and topic modeling on Twitter and Facebook posts of nine public sector organizations operating in Northeast US. The study objective is to compare and contrast message sentiment, content and topics of discussion on social media. We discover that sentiment and frequency of messages on social media is indeed affected by nature of organization's operations. We also discover that organizations either use Twitter for broadcasting or one-to-one communication with public. Finally we found discussion topics of organizations - identified through unsupervised machine learning - that engaged in similar areas of public service having similar topics and keywords in their public messages. Our analysis also indicates missed opportunities by these organizations when communication with public. Findings from this study can be used by public sector entities to understand and improve their social media engagement with citizens.

Original languageEnglish (US)
Pages (from-to)375-390
Number of pages16
JournalInformation Polity
Volume26
Issue number4
DOIs
StatePublished - 2021

ASJC Scopus subject areas

  • Information Systems
  • Communication
  • Sociology and Political Science
  • Public Administration

Keywords

  • Facebook
  • Twitter
  • public organizations
  • sentiment analysis
  • social media
  • topic modeling
  • unsupervised machine learning

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