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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

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

  • 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