Domain-Specific Fine-Tuning of IndoBERT for Aspect-Based Sentiment Analysis in Indonesian Travel User-Generated Content

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A5D8FWCGB" target="_blank" >RIV/00216208:11320/26:5D8FWCGB - isvavai.cz</a>

Result on the web

<a href="https://e-journal.unair.ac.id/JISEBI/article/view/63660" target="_blank" >https://e-journal.unair.ac.id/JISEBI/article/view/63660</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.20473/jisebi.11.1.30-40" target="_blank" >10.20473/jisebi.11.1.30-40</a>

Result language

angličtina

Original language name

Domain-Specific Fine-Tuning of IndoBERT for Aspect-Based Sentiment Analysis in Indonesian Travel User-Generated Content

Original language description

Background: Aspect-based sentiment analysis (ABSA) is essential in extracting meaningful insights from user-generated content (UGC) in various domains. In tourism, UGC such as Google Reviews offers essential feedback, but the challenges associated with processing in Indonesian language, including the unique linguistic characteristics, pose difficulties for automatic sentiment, and aspect detection. Recent advancements in transformer-based models, such as BERT, have shown great potential in addressing these challenges by providing context-aware embeddings. Objective: This research aimed to fine-tune IndoBERT, a pre-trained Indonesian language model, to perform information extraction and key aspect detection from tourism-related UGC. The objective was to identify critical aspects of tourism reviews and classify their sentiments. Methods: A dataset of 20,000 Google Reviews, focusing on 20 tourism destinations in DI Yogyakarta and Jawa Tengah, was collected and preprocessed. Multiple fine-tuning experiments were conducted, using a layer-freezing method by adjusting only the top layers of IndoBERT, while freezing others to determine the optimal configuration. The model's performance was evaluated based on validation loss, precision, recall, and F1-score in aspect detection and overall sentiment classification accuracy. Results: The best-performing configuration involved freezing the last six layers and fine-tuning the top six layers of IndoBERT, yielding a validation loss of 0.324. The model achieved precision scores between 0.85 and 0.89 in aspect detection and an overall sentiment classification accuracy of 0.84. Error analysis revealed challenges in distinguishing neutral and negative sentiments and in handling reviews with multiple aspects or mixed sentiments. Conclusion: The fine-tuned IndoBERT model effectively extracted key tourism aspects and classified sentiments from Indonesian UGC. While the model performed well in detecting strong sentiments, improvements are needed to handle neutral and mixed sentiments better. Future work will explore sentiment intensity analysis and aspect segmentation methods to enhance the model's performance. Keywords: Aspect-Based Sentiment Analysis, Fine-tuning, IndoBERT, Sentiment Classification, Tourism Reviews, User-Generated Content

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Information Systems Engineering and Business Intelligence

ISSN

2443-2555

e-ISSN

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Volume of the periodical

11

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

30-40

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105001868603