Product Features Categorization Using Constrained Spectral Clustering

Abstract

Opinion mining has increasingly become a valuable practice to grasp public opinions towards various products and related features. However, for the same feature, people may express it using different but related words and phrases. It is helpful to categorize these words and phrases, which are domain synonyms, under the same feature group to produce an effective opinion summary. In this paper, we propose a novel semi-supervised product features categorization strategy using constrained spectral clustering. Different from existing methods that cluster product features using lexical and distributional similarities, we exploit the morphological and contextual characteristics between product features as prior constraints knowledge to enhance the categorizing process. Experimental evaluation on real-life dataset demonstrates that our proposed method achieves better results compared with the baselines.