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
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- Kaji Nobuhiro
- Institute of Industrial Science, The University of Tokyo National Institute of Information and Communications Technology (NICT)
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- Yoshinaga Naoki
- Institute of Industrial Science, The University of Tokyo National Institute of Information and Communications Technology (NICT)
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- Kitsuregawa Masaru
- Institute of Industrial Science, The University of Tokyo National Institute of Informatics
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抄録
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.
収録刊行物
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- 自然言語処理
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自然言語処理 21 (3), 541-561, 2014
一般社団法人 言語処理学会
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詳細情報 詳細情報について
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- CRID
- 1390001204474286080
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- NII論文ID
- 130004691837
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- NII書誌ID
- AN10472659
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- ISSN
- 21858314
- 13407619
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- NDL書誌ID
- 025606946
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 使用不可