Text mining of online book reviews for non-trivial clustering of books and users

Abstract

The classification of consumable media by mining relevant text for their identifying features is a subjective process. Previous attempts to perform this type of feature mining have generally been limited in scope due having limited access to user data. Many of these studies used human domain knowledge to evaluate the accuracy of features extracted using these methods. In this thesis, we mine book review text to identify nontrivial features of a set of similar books. We make comparisons between books by looking for books that share characteristics, ultimately performing clustering on the books in our data set. We use the same mining process to identify a corresponding set of characteristics in users. Finally, we evaluate the quality of our methods by examining the correlation between our similarity metric, and user ratings.