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We propose a 'Sentiment Analysis as a Service' (SAaaS) framework that abstracts sentiments from social information services, analyses and transforms into useful information. We propose a dynamic service composition mechanism for sentiment analysis based on the social information service classification. We also propose a new quality model to assess the quality of social information services. We use social media based public health surveillance as a motivating scenario. In particular, we focus on the spatio-temporal properties of social media users' sentiments to identify the locations of disease outbreaks. Experiments are conducted on the real-world datasets. Analytical results preliminarily show the performance of our proposed approach.