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Categorizing Quality Determinants in Mining User-Generated Contents

Author

Listed:
  • Federico Barravecchia

    (Department of Management and Production Engineering (DIGEP), Politecnico di Torino, 10129 Torino, Italy)

  • Luca Mastrogiacomo

    (Department of Management and Production Engineering (DIGEP), Politecnico di Torino, 10129 Torino, Italy)

  • Fiorenzo Franceschini

    (Department of Management and Production Engineering (DIGEP), Politecnico di Torino, 10129 Torino, Italy)

Abstract

User-Generated Contents (UGCs) are gaining increasing popularity as a source of valuable information for companies to manage the quality of their products, services and Product-Service Systems (PSS). This paper aims at proposing a novel approach to identify and categorize quality determinants through the analysis of an extensive database of UGCs. In detail, this paper applies a topic modeling algorithm (Structural Topic Model) to identify quality determinants and introduces the Mean Rating Proportion measurement for their classification into three categories: negative, positive and neutral quality determinants. The application of the proposed methodology is exemplified through the analysis of a PSS case study (car-sharing).

Suggested Citation

  • Federico Barravecchia & Luca Mastrogiacomo & Fiorenzo Franceschini, 2020. "Categorizing Quality Determinants in Mining User-Generated Contents," Sustainability, MDPI, vol. 12(23), pages 1-11, November.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:9944-:d:452451
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    References listed on IDEAS

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    1. repec:cdl:itsrrp:qt2zv240pp is not listed on IDEAS
    2. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    3. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    4. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    5. Boyacı, Burak & Zografos, Konstantinos G. & Geroliminis, Nikolas, 2015. "An optimization framework for the development of efficient one-way car-sharing systems," European Journal of Operational Research, Elsevier, vol. 240(3), pages 718-733.
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