There are a ridiculous number of obvious things you can do with tweet data, and if you get the least bit creative, the possibilities become beyond ridiculous. We’ve barely scratched the surface in this chapter. An entire book could quite literally be devoted to systematically working through more of the possibilities, and many small businesses focused around tweet analytics could potentially turn a fairly healthy profit by selling answers to certain classes of ad-hoc queries that customers could make. Here are interesting ideas that you could pursue:
Define a similarity metric and use it to compare or further analyze two Twitter users or groups of users. You’ll essentially be developing a profile and then measuring how well a particular user fits that profile. For example, do certain hashtags, keywords (e.g., LOL, OMG, etc.), or similar metrics appear in #JustinBieber tweets more than in more intellectual tweets like ones associated with #gov20? This question might seem obvious, but what interesting things might you learn along the way while calculating a quantifiable answer?
Which celebrity Twitterers have a very low concentration of tweet entities yet a high tweet volume? Does this make them ramblers—you know, those folks who tweet 100 times a day about nothing in particular? Conversely, which celebrity Twitterers have a high concentration of hashtags in their tweets? What obvious comparisons can you make between the two groups?
If you have a Twitter account and a ...