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

Aug 3, 2026

Airport service quality ratings published on social platforms such as Airline Quality and Google Maps provide valuable signals for airport management to enhance service performance. Existing work still lacks approaches that analyze airport services within reviews using aspect-level sentiment reasoning. This study builds multiclass models with Aspect-Based Sentimental Analysis to tag traveller feedback as positive, negative, or non-existent sentiments across seven commonly studied airport services. Multiple deep learning and machine learning classifiers are trained, evaluated, and compared using Twitter, Google Maps, and Airline Quality data. Results indicate Random Forest outperforms deep learning for multiclass airport service quality prediction. The findings support multiclass machine learning for identifying sentiment drivers and improvement priorities.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

Airport services and traveller experience

Importance of online reviews

Limitations in existing sentiment analysis approaches

Methodology

Aspect-based sentiment tagging

Data sources and labelled airport services

Classification models and experimental comparison

Results and Discussion

Multiclass prediction performance

Machine learning vs deep learning comparison

Conclusion

Practical implications for airport service improvement

mathematics Article Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning Mohammed Saad M. Alanazi, Jun Li * and Karl W. Jenkins School of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Cranfield MK43 0AL, UK; m.s.alanazi@cranfield.ac.uk (M.S.M.A.); k.w.jenkins@cranfield.ac.uk (K.W.J.) * [Correspondence: jun.li@cranfield.ac.uk](Correspondence: jun.li@cranfield.ac.uk) Citation: Alanazi, M.S.M.; Li, J.; Jenkins, K.W. Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning. Mathematics 2024, 12, 781 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)math12050781 Academic Editors: Debo Cheng, Junbo Ma and Rongyao Hu Received: 7 December 2023 Revised: 26 February 2024 Accepted: 2 March 2024 Published: 6 March 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . Abstract: 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 architecturesand 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. Keywords: airport service quality; Deep Learning; Twitter; Google Maps; Airline Quality MSC: 68T07; 68T50 1. Introduction The term “airport services” encompasses a multifaceted array of offerings that collectively contribute to the overall experience of air travellers. The current literature provides various definitions and perspectives on the constituents of airport services, but often emphasises the comprehensive nature of the travellers’ experience within airport facilities. For instance, researchers (e.g., [1]) asserted that airport services encompass a spectrum of amenities and processes, including check-in procedures, security protocols, baggage handling, and terminal facilities. In contrast, other scholars (e.g., [2]), adopted a more nuanced approach to defining airport services. Their focus extended beyond the procedural elements to include the quality of passenger interactions, emphasising customer service, staff responsiveness, and the overall ambiance of the airport environment. This perspective underscores the significance of human-centric factors in shaping travellers’ experience. Additionally, Dhini and Kusumaningrum [3] delved into the concept of airport services asa holistic system that encompassed both tangible and intangible elements. Tangible aspects involve physical facilities and infrastructure, while intangib

FAQs

What is the goal of this research on airport service online reviews?

The study aims to perform multiclass sentiment prediction of airport service quality by analyzing travellers’ online reviews at the aspect level.

How does the proposed approach represent sentiment classes?

It tags sentiments for major airport services as positive, negative, or non-existent within travellers’ feedback.

Which models perform best for multiclass prediction, and what is the key conclusion?

Traditional machine learning, especially Random Forest, outperforms deep learning models for multiclass prediction of airport service quality using traveller feedback.

Aug 3, 2026

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