Service quality is essential to increase and maintain user loyalty to the railway system. In the literature, surveys have been used to measure user satisfaction, and mathematical methods have been applied to quantify the survey results. In recent years, user-generated content, including comments and complaints shared via social media, has been used to measure the quality of rail services. This content may provide important insights into the quality of the service provided with its dynamic structure. In this study, a SERVQUAL-based social-media analytics approach is used to measure railway service quality, placing special emphasis on the temporal variations in a national rail system. Topic modeling was used to assign each content item to the relevant service dimension and sentiment analysis was applied to measure the level of satisfaction. Importance–performance analysis was employed at the final stage to generate policy suggestions. Gathering more than 2.3 million social-media messages posted from 2011 to 2021, we examined the temporal evolution of service quality of the Turkish rail system. The results reveal the most and least important services and the satisfaction level of each dimension. The differences between the priorities of conventional and high-speed rail passengers are defined, and policy recommendations are presented.
KuoC.-W.TangM. L.Relationships among Service Quality, Corporate Image, Customer Satisfaction, and Behavioral Intention for the Elderly in High Speed Rail Services. Journal of Advanced Transportation, Vol. 47, 2013, pp. 512–525.
2.
ChouP.-F.LuC.-S.ChangY.-H.Effects of Service Quality and Customer Satisfaction on Customer Loyalty in High-Speed Rail Services in Taiwan. Transportmetrica A: Transport Science, Vol. 10, No. 10, 2014, pp. 917–945.
3.
YilmazV.AriE.The Effects of Service Quality, Image, and Customer Satisfaction on Customer Complaints and Loyalty in High-Speed Rail Service in Turkey: A Proposal of the Structural Equation Model. Transportmetrica A: Transport Science, Vol. 13, No. 1, 2017, pp. 67–90.
4.
CavanaR. Y.CorbettL. M.LoY. L. G.Developing Zones of Tolerance for Managing Passenger Rail Service Quality. International Journal of Quality & Reliability Management, Vol. 24, No. 1, 2007, pp. 7–31
5.
OnaD. R.EboliL.MazzullaG.Key Factors Affecting Rail Service Quality in the Northern Italy: A Decision Tree Approach. Transport, Vol. 29, No. 1, 2014, pp. 75–83.
6.
AlpuO.A Methodology for Evaluating Satisfaction with High-Speed Train Services: A Case Study in Turkey. Transport Policy, Vol. 44, 2015, pp. 151–157.
7.
EboliL.MazzullaG.Relationships between Rail Passengers’ Satisfaction and Service Quality: A Framework for Identifying Key Service Factors. Public Transport, Vol. 7, 2015, pp. 185–201.
8.
HadiuzzamanM. D.FaraziN. P.HossainS.MalikD. G. M.An Exploratory Analysis of Observed and Latent Variables Affecting Intercity Train Service Quality in Developing Countries. Transportation, Vol. 46, 2019, pp. 1447–1466.
9.
MandhaniJ.NayakJ. K.ParidaM.Interrelationships among Service Quality Factors of Metro Rail Transit System: An Integrated Bayesian Networks and PLS-SEM Approach. Transportation Research Part A, Vol. 140, 2020, pp. 320–336.
10.
AwasthiA.ChauhanS. S.OmraniH.PanahiA.A Hybrid Approach Based on SERVQUAL and Fuzzy Topsis for Evaluating Transportation Service Quality. Computers & Industrial Engineering, Vol. 61, 2011, pp. 637–646.
11.
CelikE.AydinN.Taskin GumusA.A Multiattribute Customer Satisfaction Evaluation Approach for Rail Transit Network: A Real Case Study for Istanbul, Turkey. Transport Policy, Vol. 36, 2014, pp. 283–293.
12.
KhorshidiH. A.NikfalazarS.GunawanI.Statistical Process Control Application on Service Quality Using SERVQUAL and QFD with a Case Study in Trains’ Services. The TQM Journal, Vol. 28, No. 2, 2016, pp. 195–215.
13.
AydinN.A Fuzzy-Based Multi-Dimensional and Multi-Period Service Quality Evaluation Outline for Rail Transit Systems. Transport Policy, Vol. 55, 2017, pp. 87–98.
14.
Gal-TzurA.Grant-MullerS. M.KuflikT.MinkovE.NoceraS.ShoorI.The Potential of Social Media in Delivering Transport Policy Goals. Transport Policy, Vol. 32, 2014, pp. 115–123.
15.
NisarT. M.PrabhakarG.Trains and Twitter: Firm Generated Content, Consumer Relationship Management and Message Framing. Transportation Research Part A, Vol. 113, 2018, pp. 318–334.
16.
CollinsC.HasanS.UkkusuriS. V.A Novel Transit Rider Satisfaction Metric: Rider Sentiments Measured from Online Social Media Data. Journal of Public Transportation, Vol. 16, No. 2, 2013, pp. 21–45.
17.
MogajiE.ErkanI.Insight into Consumer Experience on UK Train Transportation Services. Travel Behaviour and Society, Vol. 14, 2019, pp. 21–33.
18.
Osorio-ArjonaJ.HorakJ.SvobodaR.García-RuízY.Social Media Semantic Perceptions on Madrid Metro System: Using Twitter Data to Link Complaints to Space. Sustainable Cities and Society, Vol. 64, 2021, p. 102530.
19.
MishraD. N.PandaR. K.Decoding Customer Experiences in Rail Transport Service: Application of Hybrid Sentiment Analysis. Public Transport, Vol. 15, 2022, pp. 31–60. https://doi.org/10.1007/s12469-021-00289-7
20.
NepalS.ParisC.GeorgakopoulosD.Social Media for Government Services: An Introduction. In Social Media for Government Services (NepalS.ParisC.GeorgakopoulosD., eds.), Springer, Cham, 2015, pp. 3–24.
21.
RathoreA. K.IlavarasanP. V.Pre- and Post-Launch Emotions in New Product Development: Insights from Twitter Analytics of Three Products. International Journal of Information Management, Vol. 50, 2020, pp. 111–127.
22.
LockO.PettitC. (2020). Social Media as Passive Geo-Participation in Transportation Planning—How Effective are Topic Modeling & Sentiment Analysis in Comparison with Citizen Surveys?Geo-Spatial Information Science, Vol. 23, No. 4, 2020, pp. 275–292.
23.
ShirgaokarM.ReynardD.CollinsD.Using Twitter to Investigate Responses to Street Reallocation during COVID-19: Findings from the U.S. and Canada. Transportation Research Part A, Vol. 154, 2021, pp. 300–312.
24.
PenderB.CurrieG.DelboscA.ShiwakotiN.Social Media Use during Unplanned Transit Network Disruptions: A Review of Literature. Transport Reviews, Vol. 34, No. 4, 2014, pp. 501–521.
25.
HowardJ. M.Trains, Twitter and the Social License to Operate: An Analysis of Twitter Use by Train Operating Companies in the United Kingdom. Case Studies on Transport Policy, Vol. 8, 2020, pp. 812–821.
26.
CasasI.DelmelleE. C.Tweeting about Public Transit—Gleaning Public Perceptions from a Social Media Microblog. Case Studies on Transport Policy, Vol. 5, 2017, pp. 634–642.
27.
HosseiniM.El-DirabyT.ShalabyA.Supporting Sustainable System Adoption: Socio-Semantic Analysis of Transit Rider Debates on Social Media. Sustainable Cities and Society, Vol. 38, 2018, pp. 123–136.
28.
El-DirabyT.ShalabyA.HosseiniM.Linking Social, Semantic and Sentiment Analyses to Support Modeling Transit Customers’ Satisfaction: Towards Formal Study of Opinion Dynamics. Sustainable Cities and Society, Vol. 49, 2019, p. 101578.
29.
Grant-MullerS. M.Gal-TzurA.MinkovE.KuflikT.NoceraS.ShoorI.Transport Policy: Social Media and User-Generated Content in a Changing Information Paradigm. In Social Media for Government Services (NepalS.ParisC.GeorgakopoulosD., eds.), Springer, Cham, 2015, pp. 325–366.
30.
HabibM. A.AnikA. H.Impacts of COVID-19 on Transport Modes and Mobility Behavior: Analysis of Public Discourse in Twitter. Transportation Research Record, 2023. 2677: 65–78.
31.
KazancıoğluF. Ş.Türkiye Cumhuriyeti Devlet Demiryollarıİşletmesinin Performans Değerlendirmesi. Journal of the Faculty of Engineering and Architecture of Gazi University, Vol. 27, No. 1, 2012, pp. 219–228.
32.
Irmak KarakayaF.Arıkan ÖztürkE.Türkiye’de Yüksek Hızlı Demiryolu (YHD) Gelişimi, Gelir Gider Durumu Ve Maliyet Karşılama Kapasitesi, ATLAS Journal, Vol. 7, No. 43, 2021, pp. 2051–2065.
33.
PoyrazK.TaşkınE.KaraH.Demiryolları Işletmesi’nde Müşteri Tatmininin Ölçülmesi ve Bir Uygulama. Sosyal Bilimler Dergisi, Vol. 2, 2004, pp. 73–88.
34.
SeçilmişC.KaşlıM.KılıçlarA.SarıY.Demiryolu Hizmetlerindeki Kalitenin Ödenen Ücret Açısından Müşteri Tatminine Etkisi. Ege Akademik Bakış, Vol. 11, No. 4, 2011, pp. 573–584.
35.
LeblebicioğluB.KeskinA.Hizmet Kalitesinin Müşteri Sadakatine Etkisinde Kurum İmajı ve Müşteri Memnuniyetinin Aracı Rolü: Türkiye’deki Yüksek Hızlı Tren Hizmetleri Üzerine Bir Araştırma. Uluslararası Toplum Araştırmaları Dergisi, Vol. 18, No. 43, 2021, pp. 6425–6444.
36.
HaghighiN. N.LiuX. C.WeiR.LiW.ShaoH.Using Twitter Data for Transit Performance Assessment: A Framework for Evaluating Transit Riders’ Opinions about Quality of Service. Public Transport, Vol. 10, 2018, pp. 363–377.
37.
LuoS.HeS. Y.Using Data Mining to Explore the Spatial and Temporal Dynamics of Perceptions of Metro Services in China: The Case of Shenzhen. Urban Analytics and City Science, Vol. 48, No. 3, 2021, pp. 449–466.
38.
YangJ.AnwarA. H. M.Social Media Analysis on Evaluating Organisational Performance: A Railway Service Management Context. IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, Auckland, New Zealand, IEEE, New York, NY, 2016, pp. 835–841.
39.
EnginV.Rumeli Demiryolları. In Osmanlı’da Ulaşım (EnginV.UçarA.DoğanO., eds.), Çamlıca Basım Yayın, İstanbul, 2013, pp. 219–236.
40.
BakırcıM.Türkiye’nin Uluslararası Ulaşımında Demiryolu Sınır Kapılarının Yeri ve Etkinliği. Marmara Coğrafya Dergisi, Vol. 28, 2013, pp. 370–407.
41.
ÇetinB.BarışS.SaroğluS.Türkiye’de Karayollarının Gelişimine Tarihsel Bir Bakış, Çankırı. Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Vol. 1, No. 1, 2011, pp. 123–150.
42.
UAB. Ulaşan ve Erişen Türkiye, Ulaştırma ve Altyapı Bakanlığı, Yayınları, Ankara, 2021.
43.
AydemirC.Türkiye’de Cumhuriyet Dönemi Demiryolu Ulaşımı ve Bu Konuda İzlenen Politiklar. Unpublished PhD dissertation, Marmara University, İstanbul, 1993.
44.
AkgüngörA. P.DemirelA.Türkiyedeki Ulaştırma Sistemlerinin Analizi ve Ulaştırma Politikaları. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Vol. 10, No. 3, 2004, pp. 423–430.
45.
KarpatH. K.Turkish-Soviet Relations. In Turkish Foreign Policy in Transition, 1950-1974 (KarpatK.H., ed.), E. J. Brill, Leide, Netherlands, 1975, pp. 73–107.
46.
PolatoğluM. G.Demokrat Parti Dönemi Demiryolu Politikası (1950–1960). Mavi Atlas, Vol. 9, No. 1, 2021, pp. 215–231.
47.
UAB. Ulaşan ve Erişen Türkiye 2018. Ulaştırma ve Altyapı Bakanlığı, Yayınları, Ankara, 2018.
48.
İTÜ. Ulaştırma Ana Planı Stratejisi Sonuç Raporu. İstanbul Teknik Üniversitesi, Ulaştırma ve Ulaşım Araçlari UYG-AR, Merkezi, İstanbul, 2005.
49.
TCDD. Annual Statistics, Strateji Geliştirme Dairesi Başkanlığıİstatistik ve Analiz Şubesi, 2020.
ZeybekH.Customer Segmentation Strategy for Rail Freight Market: The Case of Turkish State Railways. Research in Transportation Business & Management, Vol. 28, 2018, pp. 45–53.
GöçgünM.6461 Sayılı Türkiye Demiryolu Ulaştırmasının Serbestleştirilmesi Hakkında Kanun Sonrası TCDD’nin Hukuki Statüsü, III. Uluslararası Al- Farabi Sosyal Bilimler Kongresi, Ankara, 2018.
55.
KurtV.TCDD TaşımacılıkA.Ş.Genel Müdürü Veysi Kurt ile Serbestleşme Süreci Üzerine. Demiryolu Mühendisliği, Vol. 5, 2017, pp. 28–33.
56.
CebeciA.Tüydeş-YamanH.IslamD. M. Z.Spatial Distribution of the Rail Freight Demand in Turkey Prior to Railway Reform. Research in Transportation Business & Management, Vol. 44, 2022, p. 100658.
57.
CurrieG.MuirC.Understanding Passenger Perceptions and Behaviors during Unplanned Rail Disruptions. Transportation Research Procedia, Vol. 25, 2017, pp. 4392–4402.
58.
CottrillC.GaultP.YeboahG.NelsonJ. D.AnableJ.BuddT.Tweeting Transit: An Examination of Social Media Strategies for Transport Information Management during a Large Event. Transportation Research Part C, Vol. 77, 2017, pp. 421–432.
59.
LiuY.ShiJ.How Inter-City High-Speed Rail Influences Tourism Arrivals: Evidence from Social Media Check-In Data. Current Issues in Tourism, Vol. 22, No. 9, 2019, pp. 1025–1042.
60.
NarayanaswamiS.Digital Social Media: Enabling Performance Quality of Indian Railway Services. Journal of Public Affairs, Vol. 18, 2018, pp. 1–10.
61.
MyneniM. B.DandamudiR.Harvesting Railway Passenger Opinions on Multi Themes by Using Social Graph Clustering. Journal of Rail Transport Planning & Management, Vol. 13, 2020, p. 100151.
62.
MathurS.NinanJ.VuorinenL.KeY.SankaranS.An Exploratory Study of the Use of Social Media to Assess Benefits Realization in Transport Infrastructure Projects. Project Leadership and Society, Vol. 2, 2021, p. 100010.
63.
ChangH.HuangJ.YaoW.ZhaoW.LiL.How Do New Transit Stations Affect People’s Sentiment and Activity? A Case Study Based on Social Media Data in Hong Kong. Transport Policy, Vol. 120, 2022, pp. 139–155.
64.
Al-SaharR.KlumpenhouwerW.ShalabyA.El-DirabyT.Using Twitter to Gauge Customer Satisfaction Response to a Major Transit Service Change in Calgary, Canada. Transportation Research Record. 2023. 0(0). https://doi.org/10.1177/03611981231179167
65.
SaragihM. H.GirsangA. S.Sentiment Analysis of Customer Engagement on Social Media in Transport Online. International Conference on Sustainable Information Engineering and Technology (SIET), Malang, Indonesia, IEEE, New York, NY, 2017, pp. 24–29.
66.
ShardaR.DelenD.TurbanE.Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. Pearson Education, NJ, 2020.
CebeciH. İ.Sosyal Medya Verileri ile Duygu Analizi. In Mühendislikte Yapay Zeka Uygulamaları (GülseçenS.İnalM.M.TorkulO.Selviİ. H.ErolÇ.ÇağılG.ReisA. Z.ve UçarK., eds.), Sakarya Üniversitesi Yayınevi, Sakarya, Türkiye, 2020, pp. 191–212.
69.
HaddiE.LiuX.ShiY.The Role of Text Pre-Processing in Sentiment Analysis. Procedia Computer Science, Vol. 17, 2013, pp. 26–32.
70.
KrouskaA.TroussasC.VirvouM. (2016). The Effect of Preprocessing Techniques on Twitter Sentiment Analysis. 7th International Conference on Information, Intelligence, Systems & Applications (IISA), Chalkidiki, Greece, IEEE, New York, NY, 2016, pp. 1–5.
71.
AnandarajanM.HillC.NolanT.Practical Text Analytics. Maximizing the Value of Text Data. (Advances in Analytics and Data Science. Vol. 2.). Springer, Switzerland, 2019.
72.
AizawaA.An Information-Theoretic Perspective of tf–idf Measures. Information Processing & Management, Vol. 39, No. 1, 2003, pp. 45–65.
PandurM. B.DobšaJ.KroneggerL. (2020). Topic Modelling in Social Sciences—Case Study of Web of Science. Central European Conference on Information and Intelligent Systems, Varaždin, Croatia, University of Zagreb, Faculty of Organization and Informatics Varaždin, pp. 211–218.
75.
ParasuramanA.ZeithamlV. A.BerryL. L.SERVQUAL: A Multiple-Item Scale for Measuring Perceptions of Service Quality. Journal of Retailing, Vol. 64, 1988, pp. 2–40.
76.
DietterichT.Overfitting and Undercomputing in Machine Learning. ACM Computing Surveys (CSUR), Vol. 27, No. 3, 1995, pp. 326–327.
77.
KimM. J.KangD. K.Ensemble with Neural Networks for Bankruptcy Prediction. Expert Systems with Applications, Vol. 37, No. 4, 2010, pp. 3373–3379.
78.
BrownleeJ.Better Deep Learning: Train Faster, Reduce Overfitting, and Make Better Predictions. Machine Learning Mastery, 2018.
79.
PolikarR.Ensemble Learning. In Ensemble Machine Learning (ZhangC.MaY., eds.), Springer, Boston, MA, 2012, pp. 1–34
PozziF. A.FersiniE.MessinaE.LiuB. (2017). Challenges of sentiment analysis in social networks: An overview. In PozziF.A.FersiniE.MessinaE.LiuB. (Eds.) Sentiment Analysis in Social Networks (pp. 1–11). Morgan Kaufmann.
82.
YueL.ChenW.LiX.ZuoW.YinM.A Survey of Sentiment Analysis in Social Media. Knowledge and Information Systems, Vol. 60, 2019, pp. 617–663.
83.
LiuB.Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge, 2020.
84.
AlaparthiS.MishraM.BERT: A Sentiment Analysis Odyssey. Journal of Marketing Analytics, Vol. 9, No. 2, 2021, pp. 118–126.
85.
ArasG.MakaroğluD.DemirS.CakirA.An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition. Expert Systems with Applications, Vol. 182, 2021, p. 115049.
86.
OzturkA. U.CemekY.KaragozP.IronyTR: Irony Detection in Turkish Informal Texts. International Journal of Intelligent Information Technologies (IJIIT), Vol. 17, No. 4, 2021, pp. 1–18.
87.
CaelenO.A Bayesian Interpretation of the Confusion Matrix. Annals of Mathematics and Artificial Intelligence, Vol. 81, No. 3–4, 2017, pp. 429–450.
88.
SammutC.WebbG. I.Encyclopedia of Machine Learning and Data Mining. Springer Publishing Company, Incorporated, New York, NY, 2017.
89.
AydemirE.Weka ile yapay zeka. Seçkin Yayınevi, Ankara, 2018.
90.
PatroV. M.PatraM. R.Augmenting Weighted Average with Confusion Matrix to Enhance Classification Accuracy. Transactions on Machine Learning and Artificial Intelligence, Vol. 2, No. 4, 2014, pp. 77–91.
91.
TuncerT.AydemirE.An Automated Local Binary Pattern Ship Identification Method by Using Sound. Acta Infologica, Vol. 4, No. 1, 2020, pp. 57–63.
ŞahbazR. P.YükselS.The Effective Factors on Election of Transportation Mode in Turkey and Railways Compatibility. Electronic Journal of Social Sciences, Vol. 7, No. 25, 2008, pp. 197–211.
SchweitzerL.Planning and Social Media: A Case Study of Public Transit and Stigma on Twitter. Journal of the American Planning Association, Vol. 80, No. 3, 2014, pp. 218–238.
96.
AltanŞ.EdizA.Türkiye’de Yüksek Hızlı Tren (YHT) için Hizmet Kalitesinin Ölçümü. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Vol. 18, No. 3, 2016, pp. 695–720.
97.
KılıçlarA.SarıY.SeçilmişC.Yolcularin Ulaşim Araci Olarak Yüksek Hizli Treni Tercih Nedenleri Üzerine Bir Araştirma. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, Vol. 11, No. 2, 2010, pp. 195–216.
98.
MartillaJ. A.JamesJ. C.Importance-Performance Analysis. Journal of Marketing, Vol. 41, No. 1, 1977, pp. 77–79
99.
KumN.ÇetinO.Demiryolu ve Karayolu Yolcu Taşimaciliğinda Hizmet Kalitesinin Karşilaştirilmasi. Yönetim Bilimleri Dergisi, Vol. 20, No. 43, 2022, pp. 217–233.
100.
SarıY.KılıçlarA.SeçilmişC.Yüksek Hızlı Tren (YHT) Yolcularının Kişisel Değişkenler Açısından Memnuniyet Algılamalarının Değerlendirilmesi. Anatolia: Turizm Araştırmaları Dergisi, Vol. 22, No. 2, 2011, pp. 127–138.
101.
ŞenelM.ŞenelM.ÖzkaraB.The Improvements on Service Quality of Turkish Railway Corporation (TCDD) in the Process of Reorganization. Anadolu University Journal of Social Sciences, Vol. 14, No. 3, 2014, pp. 65–76.
102.
KorkmazM.Tüketicilerin Hizmet Kalitesi Beklentileri ve Algilari Arasindaki İlişkinin Analizi: Yüksek Hizli Tren Yolculari Üzerine Bir Araştirma, Unpublished masters thesis, Turkish Aeronautical Association University, Ankara, 2015.
103.
CebeciH. I.GünerS.ArslanY.AydemirE.Barriers and Drivers for Biking: What Can Policymakers Learn from Social Media Analytics?Journal of Transport & Health, 28, 2023, 101542
MirandaS.TavaresP.QueiroR.Perceived Service Quality and Customer Satisfaction: A Fuzzy Set QCA Approach in the Railway Sector. Journal of Business Research, Vol. 89, 2018, pp. 371–377.
106.
De OnaJ.De OnaR.EboliL.MazzullaG.Heterogeneity in Perceptions of Service Quality among Groups of Railway Passengers. International Journal of Sustainable Transportation, Vol. 9, No. 8, 2015, pp. 612–626.
