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Entity attribute discovery and clustering from online reviews

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Abstract

The rapid increase of user-generated content (UGC) is a rich source for reputation management of entities, products, and services. Looking at online product reviews as a concrete example, in reviews, customers usually give opinions on multiple attributes of products, therefore the challenge is to automatically extract and cluster attributes that are mentioned. In this paper, we investigate efficient attribute extraction models using a semi-supervised approach. Specifically, we formulate the attribute extraction issue as a sequence labeling task and design a bootstrapped schema to train the extraction models by leveraging a small quantity of labeled reviews and a larger number of unlabeled reviews. In addition, we propose a clustering By committee (CBC) approach to cluster attributes according to their semantic similarity. Experimental results on real world datasets show that the proposed approach is effective.

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Authors and Affiliations

  1. Fujitsu Research & Development Center, Beijing, 100025, China

    Qingliang Miao, Yao Meng & Shu Zhang

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Qiudan Li & Daniel Zeng

  3. Ricoh Software Research Center (Beijing) Co., Ltd., Beijing, 100082, China

    Hao Yu

Authors
  1. Qingliang Miao
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  2. Qiudan Li
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  3. Daniel Zeng
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  4. Yao Meng
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  5. Shu Zhang
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  6. Hao Yu
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Corresponding author

Correspondence to Qingliang Miao.

Additional information

Qingliang Miao is a researcher in Fujitsu Research & Development Center. He received his PhD in pattern recognition and intelligent systems from the Institute of Automation, Chinese Academy of Sciences, China, in 2010. His primary research interests include knowledge management and opinion mining with broad applications on the web and E-Commerce.

Qiudan Li is an associate professor of the Laboratory of Complex Systems and Intelligence Science at the Institute of Automation in the Chinese Academy of Sciences. She received her PhD in computer science from Dalian University of Technology, China in 2004. Her research interests include web mining and mobile commerce applications.

Daniel Zeng is a research professor at the Chinese Academy of Sciences in the Institute of Automation. He received his PhD in industrial administration, in 1998 from the Graduate School of Industrial Administration (renamed Tepper School of Business) and The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA. His research interests include collaborative information and knowledge management, recommender systems, intelligence, and security informatics.

Yao Meng is a senior researcher in Fujitsu Research & Development Center. She received her PhD in computer science from Harbin Institute of Technology, China. Her research focuses on natural language processing with broad applications on web information processing and machine translation.

Shu Zhang is a researcher in Fujitsu Research & Development Center. She received her PhD in computer science from Harbin Institute of Technology, China, 2008. Her research focuses on document summarization, opinion mining, and entity analysis. She has around 20 papers published in journals and conferences.

Hao Yu is a general manager in Ricoh Software Research Center (Beijing) Co., Ltd. He received his PhD in computer science from Harbin Institute of Technology, China in 1998. His research focuses on natural language processing, machine translation, machine learning, and intelligent computing.

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Miao, Q., Li, Q., Zeng, D. et al. Entity attribute discovery and clustering from online reviews. Front. Comput. Sci. 8, 279–288 (2014). https://doi.org/10.1007/s11704-014-3043-8

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  • DOI: https://doi.org/10.1007/s11704-014-3043-8

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