Passengers intentions towards self-services check-in, Kuwait airport as a case study

AlKheder, Sharaf

doi:10.1016/j.techfore.2021.120864

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…For instance, Gajewicz et al [7] assessed facility attributes like cleanliness and efficiency individually, while others considered these attributes holistically or more broadly, which may encompass amenities such as food and restaurants. Consequently, the absence of uniformity in categorising airport services results in varied lists across studies [21][22][23][24][25][26]. To address this, Alaydaa et al [27] presented a two-level category of airport services covering all explicit facilities within the airport based on the conducted review, including access, check-in and security, facilities, wayfinding, and airport environment and their sub-categories, which may well suit the Aspect-Based Sentiment Analysis of travellers' online reviews.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning

Alanazi,

Li,

Jenkins

2024

Mathematics

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Airport service quality ratings found on social media such as Airline Quality and Google Maps offer invaluable insights for airport management to improve their quality of services. However, there is currently a lack of research analysing these reviews by airport services using sentimental analysis approaches. This research applies multiclass models based on Aspect-Based Sentimental Analysis to conduct a comprehensive analysis of travellers’ reviews, in which the major airport services are tagged by positive, negative, and non-existent sentiments. Seven airport services commonly utilised in previous studies are also introduced. Subsequently, various Deep Learning architectures and Machine Learning classification algorithms are developed, tested, and compared using data collected from Twitter, Google Maps, and Airline Quality, encompassing travellers’ feedback on airport service quality. The results show that the traditional Machine Learning algorithms such as the Random Forest algorithm outperform Deep Learning models in the multiclass prediction of airport service quality using travellers’ feedback. The findings of this study offer concrete justifications for utilising multiclass Machine Learning models to understand the travellers’ sentiments and therefore identify airport services required for improvement.

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Section: Literature Reviewmentioning

confidence: 99%

Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning

Alanazi,

Li,

Jenkins

2024

Mathematics

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“…Administrative actions (e.g. departure and arrival time control) or economic incentives control demand and capacity in busy airport terminals (AlKheder, 2021). The current airport revenue comes from the air side and the ground side at the airport, starting from the management of car parks, concessions, to the air side of parking stands, aviobridge, and those are all services provided by airport managers.…”

Section: Characteristics Of Air Freightmentioning

confidence: 99%

DeterminantsAirport Revenue and Total Passengers : Airport Access, Airport Service, Pandemic Covid 19, Airlines Characteristic, and Ticket Price

Sinta

2023

EAJMR

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The goal is to test between variables X1-X5 with Y, and variables Y-Z, then the result is obtained only the variable ticket Price with the Total Passengers is insignificant, then the variable airport access with variable number of passengers is also insignificant, and variable Number of passengers with variable Airport Income is also insignificant. This research is based on tangible results in the field and at airports cannot be mentioned for any reason.

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“…For example, some studies treat check-in and security as a single category, while queuing/waiting time is classified as a feature for arrival. Additionally, some airport services are uniquely featured in specific studies, such as services cap [20], prime services [21], and airport appearance [22].…”

Section: Airport Environmentmentioning

confidence: 99%

“…[3] Services, airport appearance, check in/out services, and waiting time [22] The gap found in the current studies that employed sentimental analysis was given a polarity value (negative, positive or neutral) as the overall sentimental value for travellers' feedback. in many cases, travellers feedback contains several sentimental values (positive, negative or neutral) tagging different airport services.…”

Section: Table 2 Airport Services Reported In the Studiesmentioning

confidence: 99%

Aspect-Based Sentimental Analysis for Travellers’ Reviews

M Alaydaa,

Li,

Jinkins

2023

Advanced Computer Science and Information Technology Trends

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Airport service quality evaluation is commonly found on social media, including Google Maps. This valuable for airport management in order to enhance the quality of services provided. However; prior studies either provide general review for topics discussed by travellers or provide sentimental value to tag the entire review without specifically mentioning the airport service that is behind such value. Accordingly; this work proposes using aspect based sentimental analysis in order to provide more detailed analysis for travellers’ reviews. This works applied aspect based sentimental analysis on data collected from Google Map about Dubai and Doha airports. The results provide tangible reasons to use aspect based sentimental analysis in order to understand more the travellers and spot airport services that are in need for improvement.

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Passengers intentions towards self-services check-in, Kuwait airport as a case study

Cited by 6 publications

References 25 publications

Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning

Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning

DeterminantsAirport Revenue and Total Passengers : Airport Access, Airport Service, Pandemic Covid 19, Airlines Characteristic, and Ticket Price

Aspect-Based Sentimental Analysis for Travellers’ Reviews

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