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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Public Transport

ISSN

1613-7159

e-ISSN

1866-749X

Svazek periodika

—

Číslo periodika v rámci svazku

2022-2-22

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

30

Strana od-do

1-30

Kód UT WoS článku

000759310700001

EID výsledku v databázi Scopus

2-s2.0-85125072594