Aspect-Based Sentiment Analysis of Tourist Attractions in Labuanbajo Using the Transformer Model as a Recommendation for Improving Service Quality
DOI:
https://doi.org/10.30871/jaic.v10i1.11565Keywords:
ABSA, Natural Language Processing (NLP), IndoBERT, Tourism, Labuan Bajo, Service QualityAbstract
Labuan Bajo as super-priority destinations experience improvement visit in a number of year lastly, however quality service Not yet fully fulfil expectation tourists . Study This analyze perception traveler through Aspect-Based Sentiment Analysis (ABSA) approach using the IndoBERT model. A total of 2,564 reviews multilingual from Google Maps and TripAdvisor processed through translation, pre-processing, extraction aspects, sentiment labels automatic, and model training. Four aspects analyzed based on framework SERVQUAL theory and Tourism Destination Quality: attractions, amenities, accessibility, and price. Model evaluation was conducted using precision, recall, and F1-score per aspect. The results show performance best The amenity and attraction aspects obtained the highest and most consistent scores across all metrics (around 0.83–0.88), indicating that reviews for these two aspects were more explicit and easily mapped by the model. In contrast, the access and price aspects showed lower scores (around 0.65–0.72), indicating linguistic challenges such as implicit aspects, variations in the context of the travel experience, and figurative complaints . The study This give recommendation policy connected data based direct with model findings. Limitations such as translation noise, biased datasets dominated by review positive-neutral, and not existence baseline comparison also discussed. These results confirm that ABSA approach can help stakeholder’s policy, however Still need improvement through other models such as IndoBERTweet, mBERT, or IndoBART.
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[1] AI Anzela, “Indonesia’s Commercial Diplomacy: A Case Study Of 5 Super Priority Destinations In 2019-2022,” Diploma, “Veteran” National Development University Yogyakarta, 2023. Accessed: July 28, 2025. [Online]. Available: http://eprints.upnyk.ac.id/37933/
[2] S. Janssen, KPJ Viaene, P. Van Sprang, and K. De Schamphelaere, “SI: Janssen et al. (2023): modeling copper toxicity on brook trout populations,” 2023, doi: 10.6084/M9.FIGSHARE.22067864.V1.
[3] B. Juarto, "Indonesian News Classification Using IndoBert".
[4] E. Yulianti and NK Nissa, “ABSA of Indonesian customer reviews using IndoBERT: single-sentence and sentence-pair classification approaches,” EEI Bulletin , vol. 13, no. 5, pp. 3579–3589, Oct. 2024, doi: 10.11591/eei.v13i5.8032.
[5] V. Magnini, Z. Xiang, and D. Fesenmaier, “Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet,” Journal of Retailing and Consumer Services , Sept. 2014, doi: https://doi.org/10.1016/j.jretconser.2014.08.005.
[6] M. Abdalla and G. Hirst, "Cross-Lingual Sentiment Analysis Without (Good) Translation".
[7] RB Hadiprakoso, H. Setiawan, RN Yasa, and Girinoto, “Text preprocessing for optimal accuracy in Indonesian sentiment analysis using a deep learning model with word embedding,” Presented at the Sixth International Conference Of Mathematical Sciences (ICMS 2022), Istanbul, Turkey, 2023, p. 020050. doi: 10.1063/5.0126116.
[8] M. Pontiki et al. , “SemEval-2016 Task 5: Aspect Based Sentiment Analysis,” in Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) , S. Bethard, M. Carpuat, D. Cer, D. Jurgens, P. Nakov, and T. Zesch, Eds., San Diego, California: Association for Computational Linguistics, June 2016, pp. 19–30. doi: 10.18653/v1/S16-1002.
[9] University of Bijeljina, Bijeljina, Republic of Srpska, Bosnia and Herzegovina, S. Vujičić, L. Tomić, and B. Krstić, “Use Of The Servqual Modelto Evaluate The Quality Of Tourist Services Of Spa Tourism In Serbia,” in The Eighth International Scientific Conference Tourism And Green Investments Conference Proceedings , University of Kragujevac, Faculty of Hotel Management and Tourism in Vrnjačka Banja, July 2024, pp. 399–411. doi: 10.52370/TISC24399SV.
[10] M.A. Sharafuddin, M. Madhavan, and S. Wangtueai, “Assessing the Effectiveness of Digital Marketing in Enhancing Tourist Experiences and Satisfaction: A Study of Thailand's Tourism Services,” Administrative Sciences , vol. 14, no. 11, pp. 1–21, 2024, Accessed: July 28, 2025. [Online]. Available: https://ideas.repec.org//a/gam/jadmsc/v14y2024i11p273-d1504021.html
[11] SI Angmalisang, "The Measurement of Service Quality at Linow Lake Tourist Attraction Using Servqual Method," IJTHAP , vol. 4, no. 2, pp. 1–12, June 2021, doi: 10.32535/ijthap.v4i2.1110.
[12] University of Bangka Belitung Indonesia, S. Handayani, N. Wahyudin, University of Bangka Belitung Indonesia, K. Khairiyansyah, and University of Bangka Belitung Indonesia, “Facilities, Accessibility and Tourist Attractions on Tourist Satisfaction,” j.ilm.manaj.dan.na , vol. 20, no. 2, pp. 123–133, Oct. 2019, doi: 10.30596/jimb.v20i2.3228.
[13] CA Gumussoy and B. Koseoglu, “The Effects of Service Quality, Perceived Value and Price Fairness on Hotel Customers' Satisfaction and Loyalty,” JOEBM , vol. 4, no. 9, pp. 523–527, 2016, doi: 10.18178/joebm.2016.4.9.446.
[14] A. Conneau et al. , “XNLI: Evaluating Cross-lingual Sentence Representations,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , Brussels, Belgium: Association for Computational Linguistics, 2018, pp. 2475–2485. doi: 10.18653/v1/D18-1269.
[15] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , J. Burstein, C. Doran, and T. Solorio, Eds., Minneapolis, Minnesota: Association for Computational Linguistics, June 2019, pp. 4171–4186. doi: 10.18653/v1/N19-1423.
[16] H. Zhang, Y.-N. Cheah, OM Alyasiri, and J. An, “Exploring aspect-based sentiment quadruple extraction with implicit aspects, opinions, and ChatGPT: a comprehensive survey,” Artif Intell Rev , vol. 57, no. 2, p. 17, Jan. 2024, doi: 10.1007/s10462-023-10633-x.
[17] J. Wei and K. Zou, “EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks,” Aug. 25, 2019, arXiv : arXiv:1901.11196. doi: 10.48550/arXiv.1901.11196.
[18] F. Koto, A. Rahimi, JH Lau, and T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” Nov. 02, 2020, arXiv : arXiv:2011.00677. doi: 10.48550/arXiv.2011.00677.
[19] D. Balduino, “Oneworld Alliance: A Data Analysis on Airline Customer Reviews”.
[20] X. Gu, D. He, Y. Fu, and L. Zhang, “Aspect-based Sentiment Analysis with LSTM and BERT”.
[21] DV Thin and NLT Nguyen, “Aspect-Category based Sentiment Analysis with Unified Sequence-To-Sequence Transfer Transformers,” JCSCE , Aug. 2023, doi: 10.25073/2588-1086/vnucsce.662.
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