IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v15y2023i1d10.1007_s12469-021-00289-7.html
   My bibliography  Save this article

Decoding customer experiences in rail transport service: application of hybrid sentiment analysis

Author

Listed:
  • Dibya Nandan Mishra

    (National Institute of Technology Rourkela)

  • Rajeev Kumar Panda

    (National Institute of Technology Rourkela)

Abstract

The paper aims to enhance customers’ satisfaction levels by identifying improvements in the service quality of the rail transport industry in developing countries such as India. A multi-algorithmic combination of a LEXICON analysis and a Naïve Bayes machine learning hybrid approach to sentiment analysis is performed for identifying passengers’ opinions on the services provided by Indian Railways. Inputs were gathered from the Twitter microblogging platform. Data analysis reveals that the ticket reservation and refund process, delay in operational activities, and abhorrent behavior of staff were crucial areas in which Indian Railway service needs improvement. The study imparts a conceptual methodology/process for implementing a hybrid multi-algorithmic LEXICON and machine learning techniques in sentiment analysis. The model proves to take less time to process, train, and test data than stand-alone LEXICON or machine learning-based approaches. Managers, practitioners, and researchers may use this approach to understand customer experience especially in rail transportation but also across hospitality sectors such as hotels, restaurants, education, and hospitals.

Suggested Citation

  • Dibya Nandan Mishra & Rajeev Kumar Panda, 2023. "Decoding customer experiences in rail transport service: application of hybrid sentiment analysis," Public Transport, Springer, vol. 15(1), pages 31-60, March.
    • Handle: RePEc:spr:pubtra:v:15:y:2023:i:1:d:10.1007_s12469-021-00289-7
    DOI: 10.1007/s12469-021-00289-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-021-00289-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12469-021-00289-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    2. Verhoef, Peter C. & Lemon, Katherine N. & Parasuraman, A. & Roggeveen, Anne & Tsiros, Michael & Schlesinger, Leonard A., 2009. "Customer Experience Creation: Determinants, Dynamics and Management Strategies," Journal of Retailing, Elsevier, vol. 85(1), pages 31-41.
    3. Prasanta K. Sahu & Gajanand Sharma & Anirban Guharoy, 2018. "Commuter travel cost estimation at different levels of crowding in a suburban rail system: a case study of Mumbai," Public Transport, Springer, vol. 10(3), pages 379-398, December.
    4. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2011. "Sentiment in Twitter events," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 406-418, February.
    5. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2011. "Sentiment in Twitter events," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 406-418, February.
    6. Michael G. Pollitt & Andrew S. J. Smith, 2002. "The restructuring and privatisation of British Rail: was it really that bad?," Fiscal Studies, Institute for Fiscal Studies, vol. 23(4), pages 463-502, December.
    7. N. Nima Haghighi & Xiaoyue Cathy Liu & Ran Wei & Wenwen Li & Hu Shao, 2018. "Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service," Public Transport, Springer, vol. 10(2), pages 363-377, August.
    8. Ravi Dandotiya & Pranav Aggarwal & Ram Gopal, 2020. "Impact of Food and Beverage Quality on Passenger Satisfaction in Indian Railways," International Journal of Customer Relationship Marketing and Management (IJCRMM), IGI Global, vol. 11(2), pages 37-52, April.
    9. Basu, Debasis & Hunt, John Douglas, 2012. "Valuing of attributes influencing the attractiveness of suburban train service in Mumbai city: A stated preference approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(9), pages 1465-1476.
    10. Quinn McNemar, 1947. "Note on the sampling error of the difference between correlated proportions or percentages," Psychometrika, Springer;The Psychometric Society, vol. 12(2), pages 153-157, June.
    11. Chatterjee, Sheshadri & Kumar Kar, Arpan, 2020. "Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India," International Journal of Information Management, Elsevier, vol. 53(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Izza Anwer & Muhammad Ashraf Javid & Muhammad Irfan Yousuf & Muhammad Farooq & Nazam Ali & Suniti Suparp & Qudeer Hussain, 2024. "Travelers’ Propensity to Use Intercity Railway Services in Emerging Economies: Significance of Passengers’ Satisfaction and Communication Technologies," Sustainability, MDPI, vol. 16(20), pages 1-24, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammad Masoud Rahimi & Elham Naghizade & Mark Stevenson & Stephan Winter, 2023. "SentiHawkes: a sentiment-aware Hawkes point process to model service quality of public transport using Twitter data," Public Transport, Springer, vol. 15(2), pages 343-376, June.
    2. Aghabayk, Kayvan & Esmailpour, Javad & Shiwakoti, Nirajan, 2021. "Effects of COVID-19 on rail passengers’ crowding perceptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 186-202.
    3. Ma, Jie & Tse, Ying Kei & Wang, Xiaojun & Zhang, Minhao, 2019. "Examining customer perception and behaviour through social media research – An empirical study of the United Airlines overbooking crisis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 192-205.
    4. Luis-Millán González & José Devís-Devís & Maite Pellicer-Chenoll & Miquel Pans & Alberto Pardo-Ibañez & Xavier García-Massó & Fernanda Peset & Fernanda Garzón-Farinós & Víctor Pérez-Samaniego, 2021. "The Impact of COVID-19 on Sport in Twitter: A Quantitative and Qualitative Content Analysis," IJERPH, MDPI, vol. 18(9), pages 1-20, April.
    5. Diana Maynard & Gerhard Gossen & Adam Funk & Marco Fisichella, 2014. "Should I Care about Your Opinion? Detection of Opinion Interestingness and Dynamics in Social Media," Future Internet, MDPI, vol. 6(3), pages 1-25, August.
    6. Thomas T. Hills & Eugenio Proto & Daniel Sgroi & Chanuki Illushka Seresinhe, 2019. "Historical analysis of national subjective wellbeing using millions of digitized books," Nature Human Behaviour, Nature, vol. 3(12), pages 1271-1275, December.
    7. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    8. Rongying Zhao & Mingkun Wei, 2017. "Impact evaluation of open source software: an Altmetrics perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 1017-1033, February.
    9. Karin Sim Smith & Richard McCreadie & Craig Macdonald & Iadh Ounis, 2018. "Regional Sentiment Bias in Social Media Reporting During Crises," Information Systems Frontiers, Springer, vol. 20(5), pages 1013-1025, October.
    10. Heleen Brans & Bert Scholtens, 2020. "Under his thumb the effect of president Donald Trump’s Twitter messages on the US stock market," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-11, March.
    11. Beatriz Barros & Ana Fernández-Zubieta & Raul Fidalgo-Merino & Francisco Triguero, 2018. "Scientific knowledge percolation process and social impact: A case study on the biotechnology and microbiology perceptions on Twitter," Science and Public Policy, Oxford University Press, vol. 45(6), pages 804-814.
    12. Benjamin Clapham & Michael Siering & Peter Gomber, 2021. "Popular News Are Relevant News! How Investor Attention Affects Algorithmic Decision-Making and Decision Support in Financial Markets," Information Systems Frontiers, Springer, vol. 23(2), pages 477-494, April.
    13. Lipizzi, Carlo & Iandoli, Luca & Ramirez Marquez, José Emmanuel, 2015. "Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams," International Journal of Information Management, Elsevier, vol. 35(4), pages 490-503.
    14. Oleg S. Nagornyy & Olessia Y. Koltsova, 2017. "Mining Media Topics Perceived as Social Problems by Online Audiences: Use of a Data Mining Approach in Sociology," HSE Working papers WP BRP 74/SOC/2017, National Research University Higher School of Economics.
    15. Varghese, Varun & Moniruzzaman, Md. & Chikaraishi, Makoto, 2023. "Environmental sustainability or equity in welfare? Analysing passenger flows of a mass rapid transit system with heterogeneous demand," Research in Transportation Economics, Elsevier, vol. 97(C).
    16. Ping-Yu Hsu & Hong-Tsuen Lei & Shih-Hsiang Huang & Teng Hao Liao & Yao-Chung Lo & Chin-Chun Lo, 2019. "Effects of sentiment on recommendations in social network," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 253-262, June.
    17. Hong-Hee Won & Woojae Myung & Gil-Young Song & Won-Hee Lee & Jong-Won Kim & Bernard J Carroll & Doh Kwan Kim, 2013. "Predicting National Suicide Numbers with Social Media Data," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-6, April.
    18. Widmar, Nicole Olynk & Bir, Courtney & Clifford, McKenna & Slipchenko, Natalya, 2020. "Social media sentimentas an additional performance measure? Examples from iconic theme park destinations," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    19. Chien-Chiang Lee & Mei-Ping Chen & Yi-Ting Peng, 2021. "Tourism development and happiness: International evidence," Tourism Economics, , vol. 27(5), pages 1101-1136, August.
    20. Sandra González-Bailón & Georgios Paltoglou, 2015. "Signals of Public Opinion in Online Communication," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 95-107, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pubtra:v:15:y:2023:i:1:d:10.1007_s12469-021-00289-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.