Abstract
Nowadays, all kinds of service-based organizations open online feedback possibilities for customers to share their opinion. Swiss National Railways (SBB) uses Facebook to collect commuters’ feedback and opinions. These customer feedbacks are highly valuable to make public transportation option more robust and gain trust of the customer. The objective of this study was to find interesting association rules about SBB’s commuters pain points. We extracted the publicly available FB visitor comments and applied manual text mining by building categories and subcategories on the extracted data. We then applied Apriori algorithm and built multiple frequent item sets satisfying the minsup criteria. Interesting association rules were found. These rules have shown that late trains during rush hours, deleted but not replaced connections on the timetable due to SBB’s timetable optimization, inflexibility of fines due to unsuccessful ticket purchase, led to highly customer discontent. Additionally, a considerable amount of dissatisfaction was related to the policy of SBB during the initial lockdown of the Covid-19 pandemic. Commuters were often complaining about lack of efficient and effective measurements from SBB when other passengers were not following Covid-19 rules like public distancing and were not wearing protective masks. Such rules are extremely useful for SBB to better adjust its service and to be better prepared by future pandemics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Handelszeitung (2019). https://www.handelszeitung.ch/unternehmen/si-kemme-am-saggsi-und-geen-am-zwai
Wong: A method for opinion mining of product reviews using association rules. In: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, ICIS 2009, November, pp. 270–274 (2009). https://doi.org/10.1145/1655925.1655973
Liang: Mining Product Problems from Online Feedback of Chinese Users. Emerald Publishing Limited (2017). https://doi.org/10.1108/K-03-2016-0048
Abuleil, S., Alsamara, K.: Using NLP approach for analyzing customer reviews, pp. 117–124 (2017). © Cs & It-Cscp 2017 https://doi.org/10.5121/Csit.2017.70112
Peska, L., Vojtas, P.: Using implicit preference relations to improve recommender systems. J. Data Semant. 6(1), 15–30 (2017)
Hilage, T., Kulkarni, R.: Application of data mining techniques to a selected business organization with special reference to buying behaviour. Int. J. Database Manag. Syst. 3(4), 169–181 (2011)
Huang, Y., Lin, S.: Applying multidimensional association rule mining to feedback-based recommendation systems. In: IEEE International Conference on Advances in Social Network Analysis and Mining, ASONAM (2011). https://doi.org/10.1109/ASONAM.2011.29
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Knowledge discovery and data mining: towards a unifying framework. In: Proceedings of the KDD 1996 (1996)
Open Refine (2020). http://www.geobib.fr/blog/2020-04-29-openrefine-detect-lang. Accessed 25 Aug 2021
CS & S, Code for Science & Society (2021). https://codeforscience.org/. Accessed 25 Aug 2021
Paperswitchcode (2021). https://paperswithcode.com/task/multi-label-classification. Accessed 25 Aug 2021
Liu, H., Yuan, C., Wang, X.: Label-wise document pre-training for multi-label text classification. In: Zhu, X., Zhang, M., Hong, Yu., He, R. (eds.) NLPCC 2020. LNCS (LNAI), vol. 12430, pp. 641–653. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60450-9_51. https://arxiv.org/abs/2008.06695
Ibrohim, M., Budi, I.: Multi-label hate speech and abusive language detection in Indonesian Twitter. In: Proceedings of the Third Workshop on Abusive Language Online, Florence, Italy (2019). https://doi.org/10.18653/v1/W19-3506
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Blatter, P., Einsele, F. (2022). Mining Association Rules in Commuter Feedback Comments from Facebook of Swiss National Railways (SBB) Using Apriori Algorithm. In: Pinto, A.L., Arencibia-Jorge, R. (eds) Data and Information in Online Environments. DIONE 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 452. Springer, Cham. https://doi.org/10.1007/978-3-031-22324-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-22324-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22323-5
Online ISBN: 978-3-031-22324-2
eBook Packages: Computer ScienceComputer Science (R0)