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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Sinaga, Sandro
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Computer Science & IT Engineering

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Transformer and text augmentation for tourism aspect-based sentiment analysis Situmeang, Samuel; Tambunan, Sarah Rosdiana; Jevania, Jevania; Simanjuntak, Mastawila Febryanti; Sinaga, Sandro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4614-4622

Abstract

The 36.98% growth in the quantity of electronic word of mouth (e-WOM) over the past five years presents opportunities for the tourism industry to understand tourists' needs and desires better when analyzed effectively. Aspect-based sentiment analysis (ABSA) is proposed as a solution, as it can identify the sentiment at a more detailed aspect level. Prior research revealed two issues in ABSA: imbalanced datasets and poor performance in representing implicit aspects and opinions. The authors proposed a combination of the bidirectional and auto-regressive transformer (BART) and bidirectional encoder representations from transformers (BERT) models. Leveraging BART capability in modeling context and BERT expertise in modeling text semantics and nuances, the author proposed an ABSA model that combines BART and BERT using the ensemble method. The experimental results reveal that combining these models significantly enhances the performance of the ABSA model, with an F1-score reaching 70%. Furthermore, text augmentation and preprocessing did not bring improvements in ABSA performance.
Co-Authors Jevania, Jevania Sarah Rosdiana Tambunan Simanjuntak, Mastawila Febryanti Situmeang, Samuel