| Popis: |
Nowadays, all kinds of service-based organizations open online feed-back possibilities for customers to share their opinion. Swiss National Railways(SBB) uses Facebook to collect commuters’ feedback and opinions. These cus-tomer feedbacks are highly valuable to make public transportation option morerobust and gain trust of the customer. The objective of this study was to find inter-esting association rules about SBB’s commuters pain points. We extracted thepublicly available FB visitor comments and applied manual text mining by build-ing categories and subcategories on the extracted data. We then applied Apriorialgorithm and built multiple frequent item sets satisfying the minsup criteria. Inter-esting association rules were found. These rules have shown that late trains duringrush hours, deleted but not replaced connections on the timetable due to SBB’stimetable optimization, inflexibility of fines due to unsuccessful ticket purchase,led to highly customer discontent. Additionally, a considerable amount of dis-satisfaction was related to the policy of SBB during the initial lockdown of theCovid-19 pandemic. Commuters were often complaining about lack of efficientand effective measurements from SBB when other passengers were not follow-ing 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 betterprepared by future pandemics. |
