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An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

Author

Listed:
  • Chen Liu

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Li Tang

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Wei Shan

    (School of Economics and Management, Beihang University, Beijing 100191, China)

Abstract

How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the “bad”, “good”, “poor”, “pretty good”, “not bad” these general collocation words.

Suggested Citation

  • Chen Liu & Li Tang & Wei Shan, 2018. "An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews," Sustainability, MDPI, vol. 10(5), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1425-:d:144538
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    References listed on IDEAS

    as
    1. Gang Ren & Taeho Hong, 2017. "Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
    2. He, Xiaofeng & Zha, Hongyuan & H.Q. Ding, Chris & D. Simon, Horst, 2002. "Web document clustering using hyperlink structures," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 19-45, November.
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