Annals of Operations Research

, Volume 236, Issue 1, pp 197–213 | Cite as

A multi-stage method for content classification and opinion mining on weblog comments

  • César Alfaro
  • Javier Cano-Montero
  • Javier Gómez
  • Javier M. Moguerza
  • Felipe OrtegaEmail author


In this paper, we illustrate how to combine supervised machine learning algorithms and unsupervised learning techniques for sentiment analysis and opinion mining purposes. To this end, we describe a multi-stage method for the automatic detection of different opinion trends. The proposal has been tested on real textual data available from comments introduced in a weblog, connected to organizational and administrative affairs in a public educational institution. The use of the described tool, given its potential impact to obtain valuable knowledge from opinion streams created by commenters, may be straightforwardly extended, for example, to the detection of opinion trends concerning policy decision making or electoral campaigns.


Multi-stage method Text classification Text analytics Opinion mining Sentiment analysis k-Nearest neighbors Support vector machines 



Research supported by grants from the Spanish Ministry Science and Innovation, the Ministry of Industry Tourism and Trade and the Government of Madrid: RIESGOS-CM (Ref. CAM s2009/esp-1594),, e-COLABORA, Corporate Community, Democracy4All, EDUCALAB (Ref. IPT-2011-1071-430000) and MyUniversity.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • César Alfaro
    • 1
  • Javier Cano-Montero
    • 1
  • Javier Gómez
    • 1
  • Javier M. Moguerza
    • 1
  • Felipe Ortega
    • 1
    Email author
  1. 1.Dept. of Statistics and Operations ResearchRey Juan Carlos UniversityFuenlabradaSpain

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