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A quantitative method for opinion ratings and analysis: an event study

Author

Listed:
  • Hakim Akeb

    (ISC Paris)

  • Aldo Lévy

    (LIRSA, CNAM)

  • Mohamed Rdali

    (LIRSA, CNAM)

Abstract

The development of the Internet and the emergence of social networks led people usually to give opinions and opinions about any product they have purchased. These comments and opinions may be viewed as positive or negative. So, it becomes a real challenge for any organization to manage these comments especially when they are regarded as negative news. Opinion mining is the discipline that is able to process these opinions. It is used as a decision-making tool to develop a strategy to reduce the number of negative comments or simply to compute general tendencies. In this paper, we study the case of opinions left by non-resident customers of a luxury hotel situated in a touristic country. Different tools and quantitative analysis, including codification, linear correlation as well as a multiple correspondence analysis, are implemented enabling to search for the reason of negative opinions. The main results indicated that negative ratings depend on negative perception of the service and/or the welcome and do not depend on journey type. Financial impact has been also studied.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:313:y:2022:i:2:d:10.1007_s10479-021-04023-1
    DOI: 10.1007/s10479-021-04023-1
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    References listed on IDEAS

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    1. Lian, Ying & Dong, Xuefan & Liu, Yijun, 2017. "Topological evolution of the internet public opinion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 567-578.
    2. 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.
    3. 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.
    4. Sanja Petrovic, 2019. "“You have to get wet to learn how to swim” applied to bridging the gap between research into personnel scheduling and its implementation in practice," Annals of Operations Research, Springer, vol. 275(1), pages 161-179, April.
    5. Loukas Kipenis & Dimitris Askounis, 2016. "Assessing e-Participation via user’s satisfaction measurement: the case of OurSpace platform," Annals of Operations Research, Springer, vol. 247(2), pages 599-615, December.
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