Collective Sentiment Classification Based on User Leniency and Product Popularity

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  • Gao Wenliang
    Graduate School of Information Science and Technology, The University of Tokyo
  • Kaji Nobuhiro
    Institute of Industrial Science, The University of Tokyo National Institute of Information and Communications Technology (NICT)
  • Yoshinaga Naoki
    Institute of Industrial Science, The University of Tokyo National Institute of Information and Communications Technology (NICT)
  • Kitsuregawa Masaru
    Institute of Industrial Science, The University of Tokyo National Institute of Informatics

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Abstract

We propose a method of collective sentiment classification that assumes dependencies among labels of an input set of reviews. The key observation behind our method is that the distribution of polarity labels over reviews written by each user or written on each product is often skewed in the real world; intolerant users tend to report complaints while popular products are likely to receive praise. We encode these characteristics of users and products (referred to as user leniency and product popularity) by introducing global features in supervised learning. To resolve dependencies among labels of a given set of reviews, we explore two approximated decoding algorithms, “easiest-first decoding” and “two-stage decoding.” Experimental results on real-world datasets with user and/or product information confirm that our method contributed greatly to classification accuracy.

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