Mining Online Book Reviews for Sentimental Clustering
Published 1 March 2013
Eric Lin, Shiaofen Fang, Jie Wang
Citations11
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TL;DR
This paper mine book review text to identify nontrivial features of a set of similar books to identify a corresponding set of characteristics in users, and makes comparisons between books by looking for books that share characteristics, ultimately performing clustering on the books in the data set.
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 to 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 paper, 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.
Keywords
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