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

Dibya Nandan Mishra (dibyanandanmishra@gmail.com) and Rajeev Kumar Panda
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Dibya Nandan Mishra: National Institute of Technology Rourkela
Rajeev Kumar Panda: National Institute of Technology Rourkela

Public Transport, 2023, vol. 15, issue 1, No 2, 60 pages

Abstract: 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.

Keywords: Sentiment analysis; Machine learning; Naïve Bayes; Twitter; Customer experience; Transportation; Rail (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12469-021-00289-7

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