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Social media is rapidly becoming an essential platform for social connectivity and information diffusion leading to more efficient service operations. Despite the emergent interest in studying the online behavior of social media users from different viewpoints, such as Sentiment Analysis, Topic Modeling, Emotion Mining, and Social Network Analysis, a gap remains in utilizing all such methods concurrently toward building a comprehensive analytical model. In this chapter, the mentioned aspects are combined to propose a practical analytics framework for gaining more actionable insights from social media content. In doing so, the framework is developed based on a series of machine learning and data analysis algorithms along with the required ETL modules. To evaluate the performance, our framework is exposed to a real social media dataset comprising around 600,000 records. The high level of compatibility …