Abstract
Social media, such as Twitter, typically stores a large amount of user-generated content regarding different aspects of society. These contents include social events, e-commerce products, healthcare, etc. This chapter proposes a best-fitted clustering method to classify sentiment samples related to healthcare topics. Thus, we examine other clustering models with keyword extraction methods on the real healthcare datasets collected from Twitter. The experiment results indicate that self-organized map model with the TF-IDF extraction method can achieve the best clustering accuracy. Moreover, the optimized model can have great potential to handle large-scale data in real practice.
The research work in this chapter was supported by our research team members: Chenhao Wang, Erfan Wang, Zhe Hua, and Zhengrui Xue.
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Pourroostaei Ardakani, S., Cheshmehzangi, A. (2023). Optimized Clustering Model for Healthcare Sentiments on Twitter: A Big Data Analysis Approach. In: Big Data Analytics for Smart Transport and Healthcare Systems. Urban Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-99-6620-2_9
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