Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning
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 a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/12/5/781 |
| _version_ | 1797264147123011584 |
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| author | Mohammed Saad M. Alanazi Jun Li Karl W. Jenkins |
| author_facet | Mohammed Saad M. Alanazi Jun Li Karl W. Jenkins |
| author_sort | Mohammed Saad M. Alanazi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-25T00:24:16Z |
| format | Article |
| id | doaj.art-534b2308f20d4b74bff97d0cfe61b487 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-04-25T00:24:16Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-534b2308f20d4b74bff97d0cfe61b4872024-03-12T16:50:20ZengMDPI AGMathematics2227-73902024-03-0112578110.3390/math12050781Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine LearningMohammed Saad M. Alanazi0Jun Li1Karl W. Jenkins2School of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Cranfield MK43 0AL, UKSchool of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Cranfield MK43 0AL, UKSchool of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Cranfield MK43 0AL, UKAirport 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.https://www.mdpi.com/2227-7390/12/5/781airport service qualityDeep LearningTwitterGoogle MapsAirline Quality |
| spellingShingle | Mohammed Saad M. Alanazi Jun Li Karl W. Jenkins Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning Mathematics airport service quality Deep Learning Google Maps Airline Quality |
| title | Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning |
| title_full | Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning |
| title_fullStr | Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning |
| title_full_unstemmed | Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning |
| title_short | Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning |
| title_sort | multiclass sentiment prediction of airport service online reviews using aspect based sentimental analysis and machine learning |
| topic | airport service quality Deep Learning Google Maps Airline Quality |
| url | https://www.mdpi.com/2227-7390/12/5/781 |
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