Status : Verified
| Personal Name | Dionisio, Britney M.; Galicia, Raymund F. |
|---|---|
| Resource Title | Understanding Air travel Experience Through Sentiment Analysis and Topic Modeling |
| Date Issued | July 2026 |
| Abstract | Reviews are an avenue to better understand how air travel is perceived by travelers. They contain personal evaluations and experiences, good or bad, and creates a frame of input. This study explored the use of airline reviews from Skytrax's air travel review website to assess the public's perception towards air travel and identify talking points available in the compilation of reviews. A combination of text and image data was for unimodal, bimodal early fusion, and bimodal late fusion to predict review sentiments, while Latent Dirichlet Allocation and BERTopic were used to generate topic clusters. In summary, recently published reviews on airlines with image attachments highlighted never experiences in air travel. Dissatisfaction was mainly attributed to airport and flight timing and baggage handling and fees. Despite the decline in positive sentiment, premium cabin classes and certain airline regions fared comparatively better, while seating, amenities, and Asia-Pacific routes report positively experiences. |
| Degree Course | Professional Master in Data Science (Analytics) |
| Language | English |
| Keyword | Sentiment Analysis, Topic Modeling, Airline Reviews, Bimodal Fusion, Skytrax |
| Material Type | Thesis/Dissertation |
Preliminary Pages
16.70 Mb
Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access