A tweet is far more than just 140 characters, and Twitter offers quite a lot to play with for a social network. We covered a lot of ground in this chapter by looking at many concepts and solving use cases based on real Twitter data. We learned about different Twitter objects and its APIs. We created an app of our own and utilized R's twitteR package to connect and tap into its APIs. We performed trend analysis to understand how a hashtag is used by tweeple and its temporal affects. We also solved a use case involving sentiment analysis. Through this use case, we first understood the key concepts related to sentiment analysis and then employed them to understand what emotions @POTUS conveys through his tweets. We also performed hierarchical clustering of tweets to visualize common themes using a dendrogram. The final use case analyzed Twitter from a network/graph analysis stand point. We utilized R's different libraries to prepare a network map of followers and perform analysis over it. We closed the chapter by reiterating the challenges related to social media analysis in general and Twitter in particular. With this chapter, we have set the tone and pace for the upcoming ones.

The next set of chapters will be building upon these concepts and workflows and will help us analyze other interesting social networks! #LetTheGamesBegin!