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

Author

Listed:
  • César Alfaro

    (Rey Juan Carlos University)

  • Javier Cano-Montero

    (Rey Juan Carlos University)

  • Javier Gómez

    (Rey Juan Carlos University)

  • Javier M. Moguerza

    (Rey Juan Carlos University)

  • Felipe Ortega

    (Rey Juan Carlos University)

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.

Suggested Citation

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

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    2. 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.
    3. 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.
    4. Hakim Akeb & Aldo Lévy & Mohamed Rdali, 2022. "A quantitative method for opinion ratings and analysis: an event study," Annals of Operations Research, Springer, vol. 313(2), pages 625-638, June.
    5. 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.
    6. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2021. "The interconnectedness of the economic content in the speeches of the US Presidents," Annals of Operations Research, Springer, vol. 299(1), pages 593-615, April.
    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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