Extracting key drivers of sky ratings and evaluating air passenger's satisfaction classification model through online review analysis

Luqi Xu; Xinyuan Zhang

doi:10.1117/12.3030084

8 May 2024 Extracting key drivers of sky ratings and evaluating air passenger's satisfaction classification model through online review analysis

Luqi Xu, Xinyuan Zhang

Author Affiliations +

Proceedings Volume 13162, Fourth Symposium on Pattern Recognition and Applications (SPRA 2023); 131620C (2024) https://doi.org/10.1117/12.3030084
Event: Fourth Symposium on Pattern Recognition and Applications (SPRA2023), 2023, Napoli, Italy

Abstract

The aviation industry is a vital part of modern global travel; it is obvious that aviation review is worthy of further study. In our study, we mainly intend to explore whether we can use sentiment representation of reviews instead of the raw sentences, to assist the evaluation of customer satisfaction. Two sets of experiments were conducted, one with the reviews and one with their sentiment representation. Both sets employ deep learning and machine learning techniques to guarantee comprehensive study. Results show that traditional machine learning models achieved more competitive performances than deep learning models in two tasks, and the model with Gradient Boost using sentiment representation gets the best performance. The study finds that sentiment representation could serve as a viable raw material substitute further showing that a simplified approach can be used to achieve efficiencies without sacrificing accuracy for practical applications. This provides a solid reference for future studies that intend to develop fast and accurate classification models for airline reviews.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Luqi Xu and Xinyuan Zhang "Extracting key drivers of sky ratings and evaluating air passenger's satisfaction classification model through online review analysis", Proc. SPIE 13162, Fourth Symposium on Pattern Recognition and Applications (SPRA 2023), 131620C (8 May 2024); https://doi.org/10.1117/12.3030084

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