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Aspect-Specific Polarity-Aware Summarization of Online Reviews

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Web-Age Information Management (WAIM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7923))

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Abstract

With the popularity of various social media platforms, the number of online reviews towards different products and services grows dramatically. Discovering sentiments from online reviews becomes an important and challenging task in sentiment analysis. Current methods either extract aspects without separating aspects and sentiments, or extract aspects and sentiments without separating sentiments according to their polarities. In this paper, we propose two novel probabilistic generative models (APSM and ME-APSM) to extract aspects and aspect-specific polarity-aware sentiments from online reviews. We applied our models to two data sets with three different experiments. Experimental results show that APSM and ME-APSM models can extract aspects and polarity-aware sentiments well. For the sentiment classification task, our models outperform other generative models and come close to supervised classification methods.

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Author information

Authors and Affiliations

  1. Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, 100871, China

    Gaoyan Ou, Wei Chen, Peng Liu, Tengjiao Wang & Dongqing Yang

  2. School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Gaoyan Ou, Wei Chen, Peng Liu, Tengjiao Wang & Dongqing Yang

  3. The Shenzhen Key Lab for Cloud Computing Technology and Applications (SPCCTA), Peking University Shenzhen Graduate School, Shenzhen, 518055, China

    Kai Lei

  4. Department of Information Science and Technology, University of International Relations, Beijing, China

    Yueqin Liu

Authors
  1. Gaoyan Ou
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  2. Wei Chen
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  3. Peng Liu
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  4. Tengjiao Wang
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  5. Dongqing Yang
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  6. Kai Lei
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  7. Yueqin Liu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China

    Jianyong Wang

  2. Management Science and Information Systems Department, Rutgers, the State University of New Jersey, 1, Washington Park, 07102, Newark, NJ, USA

    Hui Xiong

  3. Department of Information Engineering, Nagoya University, 464-8601, Nagoya, Japan

    Yoshiharu Ishikawa

  4. Department of Computer Science, Hong Kong Baptist University, Hong Kong

    Jianliang Xu

  5. School of Information Science and Engineering, Yanshan University, Qinhuangdao, China

    Junfeng Zhou

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Ou, G. et al. (2013). Aspect-Specific Polarity-Aware Summarization of Online Reviews. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_30

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  • DOI: https://doi.org/10.1007/978-3-642-38562-9_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38561-2

  • Online ISBN: 978-3-642-38562-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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