Mining the Social Web, 2nd Edition

Chapter 4. Mining Google+: Computing Document Similarity, Extracting Collocations, and More

This chapter introduces some fundamental concepts from text mining^[13] and is somewhat of an inflection point in this book. Whereas we started the book with basic frequency analyses of Twitter data and gradually worked up to more sophisticated clustering analyses of messier data from LinkedIn profiles, this chapter begins munging and making sense of textual information in documents by introducing information retrieval (IR) theory fundamentals such as TF-IDF, cosine similarity, and collocation detection. Accordingly, its content is a bit more complex than that of the chapters before it, and it may be helpful to have worked through those chapters before picking up here.

Google+ initially serves as our primary source of data for this chapter because it’s inherently social, features content that’s often expressed as longer-form notes that resemble blog entries, and is now an established staple in the social web. It’s not hard to make a case that Google+ activities (notes) fill a niche somewhere between Twitter and blogs. The concepts from previous chapters could equally be applied to the data behind Google+’s diverse and powerful API, although we’ll opt to leave regurgitations of those examples (mostly) as exercises for the motivated reader.

Wherever possible we won’t reinvent the wheel and implement analysis tools from scratch, but we will take a couple of “deep dives” when particularly foundational ...