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Multi-dimensional Sentiment Analysis for Large-Scale E-commerce Reviews

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Book cover Database and Expert Systems Applications (DEXA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8645))

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Abstract

E-commerce reviews reveal the customers’ attitudes on the products, which are very helpful for customers to know other people’s opinions on interested products. Meanwhile, producers are able to learn the public sentiment on their products being sold in E-commerce platforms. Generally, E-commerce reviews involve many aspects of products, e.g., appearance, quality, price, logistics, and so on. Therefore, sentiment analysis on E-commerce reviews has to cope with those different aspects. In this paper, we define each of those aspects as a dimension of product, and present a multi-dimensional sentiment analysis approach for E-commerce reviews. In particular, we employ a sentiment lexicon expanding mechanism to remove the word ambiguity among different dimensions, and propose an algorithm for sentiment analysis on E-commerce reviews based on rules and a dimensional sentiment lexicon. We conduct experiments on a large-scale dataset involving over 28 million reviews, and compare our approach with the traditional way that does not consider dimensions of reviews. The results show that the multi-dimensional approach reaches a precision of 95.5% on average, and outperforms the traditional way in terms of precision, recall, and F-measure.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Lizhou Zheng, Peiquan Jin & Lihua Yue

  2. School of Business, Anhui University, 230601, Hefei, China

    Jie Zhao

  3. Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, China

    Lizhou Zheng, Peiquan Jin & Lihua Yue

Authors
  1. Lizhou Zheng
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  2. Peiquan Jin
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  3. Jie Zhao
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  4. Lihua Yue
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Editor information

Editors and Affiliations

  1. Instituto Tecnológico de Informática, 46022, Valencia, Spain

    Hendrik Decker

  2. Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague, 166 27, Prague 6, Czech Republic

    Lenka Lhotská

  3. Department of Computer Science, The University of Auckland, 1010, Auckland, New Zealand

    Sebastian Link

  4. Knowledge Management, LMU University of Munich, Leopoldstraße 13, 80802, Munich, Germany

    Marcus Spies

  5. FAW, University of Linz, Altenbergerstrasse 69, 4040, Linz, Austria

    Roland R. Wagner

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Zheng, L., Jin, P., Zhao, J., Yue, L. (2014). Multi-dimensional Sentiment Analysis for Large-Scale E-commerce Reviews. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds) Database and Expert Systems Applications. DEXA 2014. Lecture Notes in Computer Science, vol 8645. Springer, Cham. https://doi.org/10.1007/978-3-319-10085-2_41

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  • DOI: https://doi.org/10.1007/978-3-319-10085-2_41

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10084-5

  • Online ISBN: 978-3-319-10085-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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