short-paper

High Enough?: Explaining and Predicting Traveler Satisfaction Using Airline Reviews

Authors:

Dominik Kowald,

Elisabeth LexAuthors Info & Claims

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media

Pages 249 - 254

https://doi.org/10.1145/2914586.2914629

Published: 10 July 2016 Publication History

Abstract

Air travel is one of the most frequently used means of transportation in our every-day life. Thus, it is not surprising that an increasing number of travelers share their experiences with airlines and airports in form of online reviews on the Web. In this work, we thrive to explain and uncover the features of airline reviews that contribute most to traveler satisfaction. To that end, we examine reviews crawled from the Skytrax air travel review portal. Skytrax provides four review categories to review airports, lounges, airlines and seats. Each review category consists of several five-star ratings as well as free-text review content. In this paper, we conduct a comprehensive feature study and we find that not only five-star rating information such as airport queuing time and lounge comfort highly correlate with traveler satisfaction but also inferred features in the form of the review text sentiment. Based on our findings, we create classifiers to predict traveler satisfaction using the best performing rating features. Our results reveal that given our methodology, traveler satisfaction can be predicted with high accuracy. Additionally, we find that training a model on the sentiment of the review text provides a competitive alternative when no five-star rating information is available. We believe that our work is of interest for researchers in the area of modeling and predicting user satisfaction based on available review data on the Web.

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Cited By

Hasib KNaseem UKeya AMaitra SMithu KAlam M(2024)Systematic Literature Review on Sentiment Analysis in Airline IndustrySN Computer Science10.1007/s42979-024-03567-w6:1Online publication date: 23-Dec-2024
https://doi.org/10.1007/s42979-024-03567-w
Kunekar PDeshpande MGharpure AGokhale VGore AYadav H(2023)Evaluating the Predictive Ability of the LightGBM Classifier for Assessing Customer Satisfaction in the Airline Industry2023 International Conference for Advancement in Technology (ICONAT)10.1109/ICONAT57137.2023.10080120(1-6)Online publication date: 24-Jan-2023
https://doi.org/10.1109/ICONAT57137.2023.10080120
Srinivas SRamachandiran S(2023)Passenger intelligence as a competitive opportunity: unsupervised text analytics for discovering airline-specific insights from online reviewsAnnals of Operations Research10.1007/s10479-022-05162-9333:2-3(1045-1075)Online publication date: 13-Jan-2023
https://doi.org/10.1007/s10479-022-05162-9
Show More Cited By

Index Terms

High Enough?: Explaining and Predicting Traveler Satisfaction Using Airline Reviews
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification

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cover image ACM Conferences

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media

July 2016

354 pages

ISBN:9781450342476

DOI:10.1145/2914586

General Chairs:
Jamie Blustein
Dalhousie University, Canada
,
Eelco Herder
L3S Research Center, Germany
,
Program Chairs:
Jessica Rubart
OWL University of Applied Sciences, Germany
,
Helen Ashman
University of South Australia

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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New York, NY, United States

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Published: 10 July 2016

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HT '16

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SIGWEB

HT '16: 27th ACM Conference on Hypertext and Social Media

July 10 - 13, 2016

Nova Scotia, Halifax, Canada

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HT '16 Paper Acceptance Rate 16 of 54 submissions, 30%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Hasib KNaseem UKeya AMaitra SMithu KAlam M(2024)Systematic Literature Review on Sentiment Analysis in Airline IndustrySN Computer Science10.1007/s42979-024-03567-w6:1Online publication date: 23-Dec-2024
https://doi.org/10.1007/s42979-024-03567-w
Kunekar PDeshpande MGharpure AGokhale VGore AYadav H(2023)Evaluating the Predictive Ability of the LightGBM Classifier for Assessing Customer Satisfaction in the Airline Industry2023 International Conference for Advancement in Technology (ICONAT)10.1109/ICONAT57137.2023.10080120(1-6)Online publication date: 24-Jan-2023
https://doi.org/10.1109/ICONAT57137.2023.10080120
Srinivas SRamachandiran S(2023)Passenger intelligence as a competitive opportunity: unsupervised text analytics for discovering airline-specific insights from online reviewsAnnals of Operations Research10.1007/s10479-022-05162-9333:2-3(1045-1075)Online publication date: 13-Jan-2023
https://doi.org/10.1007/s10479-022-05162-9
Bakır MAkan ŞÖzdemir ENguyen PTsai JPham H(2022)How to Achieve Passenger Satisfaction in the Airport? Findings from Regression Analysis and Necessary Condition Analysis Approaches through Online Airport ReviewsSustainability10.3390/su1404215114:4(2151)Online publication date: 14-Feb-2022
https://doi.org/10.3390/su14042151
DOĞAN SÖZKUL EKAYA G(2022)Investigating Which Services are Effective on Recommendation of the Airline CompaniesAdvances in Hospitality and Tourism Research (AHTR)10.30519/ahtr.91513610:1(109-129)Online publication date: 1-Mar-2022
https://doi.org/10.30519/ahtr.915136
Brochado ADuarte MMengyuan Z(2022)Passengers’ Perceptions of Chinese Airlines’ Service Quality: A Mixed Methods Analysis of User-generated ContentJournal of China Tourism Research10.1080/19388160.2022.212264719:3(677-699)Online publication date: 11-Sep-2022
https://doi.org/10.1080/19388160.2022.2122647
Farzadnia SRaeesi Vanani I(2022)Identification of opinion trends using sentiment analysis of airlines passengers' reviewsJournal of Air Transport Management10.1016/j.jairtraman.2022.102232103(102232)Online publication date: Aug-2022
https://doi.org/10.1016/j.jairtraman.2022.102232
Tian HPresa-Reyes MTao YWang TPouyanfar SMiguel ALuis SShyu MChen SIyengar S(2021)Data Analytics for Air Travel Data: A Survey and New PerspectivesACM Computing Surveys10.1145/346902854:8(1-35)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3469028
Ouyang SLawlor A(2021)Improving Explainable Recommendations by Deep Review-Based ExplanationsIEEE Access10.1109/ACCESS.2021.30761469(67444-67455)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3076146
Zayet TIsmail MVarathan KNoor RChua HLee ALow YSingh S(2021)Investigating transportation research based on social media analysis: a systematic mapping reviewScientometrics10.1007/s11192-021-04046-2Online publication date: 24-Jun-2021
https://doi.org/10.1007/s11192-021-04046-2
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