Browsing by Author "Alanazi, Mohammed Saad M."

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    Multiclass sentiment prediction of airport service online reviews using aspect-based sentimental analysis and machine learning
    (MDPI, 2024-03-06) Alanazi, Mohammed Saad M.; Li, Jun; Jenkins, Karl W.
    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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    Predicting passengers’ feedback rate for airport service quality
    (Elsevier, 2024-02-24) Alanazi, Mohammed Saad M.; Jenkins, Karl W.; Li, Jun
    Airport service quality evaluation is commonly found on social media sites, including Google Maps. The reviews by users of Google Maps are longer in terms of the number of words than those found on Twitter. They also include a rating, whereas those on Twitter need to be labelled. However, they are less well known than those on Twitter amongst researchers who focus on sentimental analysis. This study attempts to fill the gap in the current literature and develops architecture that is based on Long-Short Term Memory Neural Networks and Convolution Neural Networks. The combined model developed receives meta-data, such as the number of words in the review and the number of likes the review receives in addition to the key review words. The two models, the first of which predicts polarity and the second reviews ratings, were tested under several variations of parameters and showed consistency in results. The dataset was collected from Google Maps and focused on two crowded airports in the Arabic Peninsula (Doha and Dubai). They were found to be unbalanced, with positive reviews being more abundant than negative reviews.