Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2021

Abstract

Smart cities, are often perceived as urban areas that use technologies to manage resources, improve economy and enhance community livelihood. In this paper, we share an approach which uses multiple sources of data for evidence-based analysis of the public's views, concerns and sentiments on the topic related to mental wellness. We hope to bring forth a better understanding of the existing concerns of the citizens and available social support. Our study leverages on social sensing via text mining and social network analysis to listen to the voices of the citizens through revealed content from web data sources, such as social media and public forums. By using hybrid data sources, we present the important considerations for mining inherent mental wellness concerns faced by the citizens. The outcome of the analysis includes, both the positive and negative sentiments towards mental wellness and draws relations to national level performance indicators relating to mental wellness. We hope our research could help authorities derive actionable plans for designing health services or public events that bring positive social mixing and happiness by addressing the mental wellness of the residents.

Keywords

Text mining, Social networking (online), Smart cities, Well-being, Analytics

Discipline

Databases and Information Systems | Urban Studies and Planning

Research Areas

Data Science and Engineering

Publication

2021 17th IEEE International Conference on Automation Science and Engineering (CASE): Lyon, France, August 23-27: Proceedings

First Page

1

Last Page

6

ISBN

9781665418720

Identifier

10.1109/CASE49439.2021.9551606

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/CASE49439.2021.9551606

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