Decoding customer experiences in rail transport service: application of hybrid sentiment analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AJWQZE4SZ" target="_blank" >RIV/00216208:11320/22:JWQZE4SZ - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s12469-021-00289-7" target="_blank" >https://doi.org/10.1007/s12469-021-00289-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s12469-021-00289-7" target="_blank" >10.1007/s12469-021-00289-7</a>
Alternative languages
Result language
angličtina
Original language name
Decoding customer experiences in rail transport service: application of hybrid sentiment analysis
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Public Transport
ISSN
1613-7159
e-ISSN
1866-749X
Volume of the periodical
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Issue of the periodical within the volume
2022-2-22
Country of publishing house
DE - GERMANY
Number of pages
30
Pages from-to
1-30
UT code for WoS article
000759310700001
EID of the result in the Scopus database
2-s2.0-85125072594