Open AccessAnalyzing social media messages of public sector organizations utilizing sentiment analysis and topic modeling
Author(s) -
Ussama Yaqub,
Soon Ae Chun,
Vijayalakshmi Atluri,
Jaideep Vaidya
Publication year - 2021
Publication title -
information polity
Language(s) - English
Resource type - Journals
eISSN - 1875-8754
pISSN - 1570-1255
DOI - 10.3233/ip-210321
Subject(s) - social media , sentiment analysis , public sector , broadcasting (networking) , public relations , content analysis , public service , computer science , world wide web , political science , sociology , artificial intelligence , computer network , social science , law
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.