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• W3175287827 abstract "Purpose One of the major challenges within the airline industry is to keep pace with the changing customer perception toward their service quality. This paper aims to demonstrate how sentiment analysis of user-generated big data can be used to research airline service quality as a more comprehensive alternative to other survey-based models by investigating real-time passenger insights. Design/methodology/approach The present research uses the case of Indigo airlines by studying passenger's trip advisor reviews regarding the low-cost commercial airline service. The authors analyzed 1,777 passenger reviews, which were classified, to uncover sentiments for five dimensions of airline service quality (AIRQUAL). Findings The findings of the study demonstrate the need for harnessing the brand-related user-generated content shared on online platforms to identify the critical attributes for airline service quality. Further, through the application of sentiment analysis, the paper provides much-needed clarity in the processing of user-generated content. It illustrates the investigation of passenger interactions as a reflection of their satisfaction, expectation, intention and overall opinion toward the airline service quality. Practical implications The analytical framework adopted in the study for examining user-generated content (UGC) can be functional for the marketing managers and equip them for handling large-scale data readily available in action-oriented interactive marketing research. Originality/value This paper demonstrates how sentiment analysis of user-generated data can be used to research airline service quality as a more comprehensive alternative to other survey-based models. The study supplements the methodological advances in the field of UGC analysis and adds to the existing knowledge domain." @default.
• W3175287827 created "2021-07-05" @default.
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• W3175287827 date "2021-06-18" @default.
• W3175287827 modified "2023-10-11" @default.
• W3175287827 title "Reading between the lines: untwining online user-generated content using sentiment analysis" @default.
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• W3175287827 doi "https://doi.org/10.1108/jrim-03-2020-0045" @default.
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