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Feature Level Mining of Online Reviews Based on a Semi-Supervised Learning Model

In: Liss 2014

Author

Listed:
  • Minxi Wang

    (Sichuan University
    Chengdu University of Technology)

  • Xin Li

    (Chengdu University of Technology)

Abstract

Online reviews are written by customers based on personal usage experience. They not only help manufacturers better understand consumer responses to their products, but also serve as a reliable source of information help other customers make purchase decision. In this paper, we propose a novel semi-supervised learning algorithm to address the feature-level reviews mining problem. The proposed method consists of three phases: (1) build a support function that characterizes the support of a multi-dimensional distribution of a given data set; (2) decompose a whole data space into a small number of separate clustered regions via a dynamical system associated with the constructed support function; (3) assign a class label to each decomposed region using the information of their constituent labeled data and the constructed dynamical system, thereby classifying in-sample unlabeled data as well as unknown out-of-sample data.

Suggested Citation

  • Minxi Wang & Xin Li, 2015. "Feature Level Mining of Online Reviews Based on a Semi-Supervised Learning Model," Springer Books, in: Zhenji Zhang & Zuojun Max Shen & Juliang Zhang & Runtong Zhang (ed.), Liss 2014, edition 127, pages 709-715, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-43871-8_102
    DOI: 10.1007/978-3-662-43871-8_102
    as

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