Soft Computing

, Volume 20, Issue 9, pp 3411–3420 | Cite as

Hierarchical classification in text mining for sentiment analysis of online news

  • Jinyan LiEmail author
  • Simon Fong
  • Yan Zhuang
  • Richard Khoury


Sentiment analysis in text mining is a challenging task. Sentiment is subtly reflected by the tone and affective content of a writer’s words. Conventional text mining techniques, which are based on keyword frequencies, usually run short of accurately detecting such subjective information implied in the text. In this paper, we evaluate several popular classification algorithms, along with three filtering schemes. The filtering schemes progressively shrink the original dataset with respect to the contextual polarity and frequent terms of a document. We call this approach “hierarchical classification”. The effects of the approach in different combination of classification algorithms and filtering schemes are discussed over three sets of controversial online news articles where binary and multi-class classifications are applied. Meanwhile we use two methods to test this hierarchical classification model, and also have a comparison of the two methods.


Sentiment analysis Text mining  Classification 



The authors are thankful for the financial support from the research Grants of Grant No. MYRG152 (Y3-L2)-FST11-ZY, and FDCT 019/2011/A1, offered by the University of Macau and Macau SAR government.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jinyan Li
    • 1
    Email author
  • Simon Fong
    • 1
  • Yan Zhuang
    • 1
  • Richard Khoury
    • 2
  1. 1.Department of Computer and Information ScienceUniversity of MacauTaipaMacau SAR
  2. 2.Department of Software EngineeringLakehead UniversityThunder BayCanada

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