Method and apparatus for unsupervised learning of multi-resolution user profile from text analysis

Abstract

A method and apparatus for retrieving information from a massive amount of user-written businesses reviews are described. From the bag of words of a given review set, a graph based on mutual information between the words is built. Spectral analysis on this graph enables creation of a Euclidean space specific to those reviews where the distance corresponds to semantic proximity. Applying a cover-tree based divisive hierarchical clustering in this space yields therefore a semantic tag tree. Such a taxonomy is specific of the review set used, which could be all the reviews about a product or written by a user, and can be used for profiling. These taxonomies are used to build profiles. Also described is a tool to summarize and browse the review set based on the obtained trees.

Dependencies are inferred from the claim text.