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

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Listed:
  • César Alfaro
  • Javier Cano-Montero
  • Javier Gómez
  • Javier Moguerza
  • Felipe Ortega

Abstract

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. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • César Alfaro & Javier Cano-Montero & Javier Gómez & Javier Moguerza & Felipe Ortega, 2016. "A multi-stage method for content classification and opinion mining on weblog comments," Annals of Operations Research, Springer, vol. 236(1), pages 197-213, January.
    • Handle: RePEc:spr:annopr:v:236:y:2016:i:1:p:197-213:10.1007/s10479-013-1449-6
    DOI: 10.1007/s10479-013-1449-6
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    References listed on IDEAS

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    1. Fred Roberts & Alexis Tsoukiàs, 2008. "Computer science and decision theory: preface," Annals of Operations Research, Springer, vol. 163(1), pages 1-4, October.
    2. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, December.
    3. Fred Roberts, 2008. "Computer science and decision theory," Annals of Operations Research, Springer, vol. 163(1), pages 209-253, October.
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    Cited by:

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    4. Li, Han & Gupta, Ashish & Zhang, Jie & Flor, Nick, 2020. "Who will use augmented reality? An integrated approach based on text analytics and field survey," European Journal of Operational Research, Elsevier, vol. 281(3), pages 502-516.
    5. Che Xu & Wenjun Chang & Weiyong Liu, 2023. "Data-driven decision model based on local two-stage weighted ensemble learning," Annals of Operations Research, Springer, vol. 325(2), pages 995-1028, June.
    6. Hui Yuan & Wei Xu & Qian Li & Raymond Lau, 2018. "Topic sentiment mining for sales performance prediction in e-commerce," Annals of Operations Research, Springer, vol. 270(1), pages 553-576, November.
    7. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2020. "The interconnectedness of the economic content in the speeches of the US Presidents," Papers 2002.07880, arXiv.org.
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    9. Horst Treiblmaier & Patrick Mair, 2021. "Textual Data Science for Logistics and Supply Chain Management," Logistics, MDPI, vol. 5(3), pages 1-15, August.

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