Discovering contextual tags from product review using semantic relatedness.

In the design community, while a number of studies that have focused on studying product reviews in various design analysis perspectives, contextual annotation of identified terms (e.g. product features) has not been fully explored. This paper proposed a learnable approach towards discovering contextual tags from product reviews. A ranking algorithm, FacetRank, is proposed to rank important key terms along with an approach to discover contextual annotation of the terms from review documents. The evaluation of our proposal is performed using two annotated corpus to examine our algorithm's contextual tagging performance. A case study using a small collection of laptop reviews is also reported to showcase how our algorithm can be applied towards product feature understanding and multi-faceted product ontology development. Finally, we conclude this paper with some indications for future work. [ABSTRACT FROM AUTHOR]