ABSTRACT
This chapter describes the systematic approach to this probing and demonstrates its application using vignettes that involve various Twitter-based communities (TBCs). It discusses the dimensional model for the design of a data warehouse for the knowledge base. The chapter argues that the underlying individual tacit knowledge and the corresponding collective tacit knowledge hidden in a user-generated social media corpus can be converted into explicit knowledge through the use of advanced analytics techniques. It discusses how one can begin to uncover collective tacit knowledge by performing analytics at the corpus level. The chapter deals with expressions of positive, negative, and neutral sentiment, opinion analyses, in general, can be undertaken to assess more complex phenomena, including emotions such as joy, sadness, anger, fear, surprise, and disgust, political leanings, and media slant. It examines the knowledge that develops within a community of Twitter users. The chapter views a corpus of Tweets produced by the TBC as the community's store of common knowledge.
