Smart City Gnosys
Smart city article details
| Title | Uncovering Concerns Of Citizens Through Machine Learning And Social Network Sentiment Analysis |
|---|---|
| ID_Doc | 59416 |
| Authors | Kumi S.; Snow C.; Lomotey R.K.; Deters R. |
| Year | 2024 |
| Published | IEEE Access, 12 |
| DOI | http://dx.doi.org/10.1109/ACCESS.2024.3426329 |
| Abstract | Artificial Intelligence and Machine Learning (AI/ML) as analytical tools can be applied across multiple social domains. Thus, these tools are being deployed in several ways to address societal issues and concerns for 'social good'. For instance, AI/ML has applicable use cases for crisis response, economic empowerment, educational demands, environmental challenges, equality and inclusion, health and hunger, and security and justice. In this work, we seek to explore the power and capability of AI/ML in understanding citizens' engagement, which can improve governance and smart city deployment. Specifically, we studied the views expressed by online users about the city of Saskatoon in Canada. The analyzed views have become a value chain that community leaders can use to improve the governance structure of the city. In the study, we extracted 114,390 comments from Reddit (i.e., Saskatoon subreddit posts) between January 1, 2019, and September 20, 2023, to discover topics to highlight citizens' concerns. We compare the performance of three major topic models, namely, Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and BERTopic with a K-means clustering algorithm in the discovery of topics from the collected Reddit comments. The BERTopic with the K-means clustering algorithm achieved the highest coherence score of approximately 0.64 in the extraction of 25 topics from the dataset. Our findings showed that BERTopic can discover coherent and diverse topics compared to LDA and NMF. We found 12 underlying themes by merging related topics. Also, we leveraged SiEBERT (a pre-trained transformer model), 4 supervised ML models, and VADER (a lexical sentiment analysis classifier) to identify the sentiments expressed in each theme. The SiEBERT model outperformed the other sentiment classifiers with an accuracy of 89% in the prediction of sentiments. The research discovered factors for smart city engagement such as Housing and Facilities, Education, Downtown Development, Tourism and Entertainment, Policing, Healthcare, Online Community, and Cost. © 2024 The Authors. |
| Author Keywords | citizens engagement; Machine learning; smart city; social media; textual mining |
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