Social media provides an infrastructure where users can share their data at an unprecedented speed without worrying about storage and processing. Social media data has grown exponentially and now there is major interest in extracting any useful information from the social media data to apply in various domains. Currently, there are various tools available to analyze the large amounts of social media data. However, these tools do not consider the diversity of the social media data, and treat

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... al media as a uniform data source with similar features. Thus, these tools lack the flexibility to dynamically process and analyze the social media data according to its diverse features. In this paper, we develop a 'Big Social Data as a Service' (BSDaaS) composition framework that extracts the data from various social media platforms, and transforms it into useful information. The framework provides a quality model to capture the dynamic features of social media data. In addition, our framework dynamically assesses the quality features of the social media data and composes appropriate services required for various information analyses. We present a social media based sentiment analysis system as a motivating scenario and conduct experiments using real-world datasets to show the efficiency of our approach.