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September 10, 2025Paladyn Journal of Behavioral RoboticsOpen Access

Air fare sentiment via Backtranslation-CNN-BiLSTM and BERTopic

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Authors

XKXijun KeJWJiajun WenHXHaiwen Xu

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Overview

This analysis uses a novel model to analyze sentiment in passenger comments on social media, suggesting improvements for airlines and airports.

Key Points

  • The backtranslation-CNN-BiLSTM model effectively analyzes sentiment in airline and airport comments.
  • Using transfer learning and TF-IDF analysis, data quality was ensured for accurate evaluations.
  • The BERTopic model revealed key insights into passenger needs and comments for service optimization.
  • Passenger comments on social media provide critical insights for improving aviation services.

Cite This Study

Ke et al. (2025) studied this question.

synapsesocial.com/papers/68c1a13354b1d3bfb60dc83fhttps://doi.org/10.1515/pjbr-2024-0005
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