Extraction of Minority Opinion Based on Peculiarity in a Semantic Space Constructed of Free Writing
Recently, the “voice of the customer (VOC)” such as exhibited by a Web user review has become easily collectable as text data. Based on large quantity of collected review data, we take the stance that minority review sentences buried in a majority review have high value. It is conceivable that hints of solution and discovery of new subjects are hidden in such minority opinions. The purpose of this research is to extract minority opinion from a huge quantity of text data taken from free writing in user reviews of products and services. In this study, we propose a method for extracting minority opinions that become outliers in a low-dimensional semantic space. Here, a low-dimensional semantic space of Web user reviews is constructed by latent semantic indexing (LSI). We were able to extract minority opinions using the Peculiarity Factor (PF) for outlier detection. We confirmed the validity of our proposal through an analysis using the user reviews of the EC site.
KeywordsText Mining Online Reviews Peculiarity Factor (PF) Dimensionality Reduction Voice of the Customer (VOC)
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