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Stacking ensemble for multilingual aspect-based sentiment analysis of tourism reviews

IAES International Journal of Artificial Intelligence · Vol. 15 No. 5 (2026) · hlm. 4381–4391

DOI
10.11591/ijai.v15.i5.pp4381-4391
ISSN
2252-8938, 2089-4872
Terbit
2026-10-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Multilingual tourism reviews often express opinions about multiple destination attributes within a single text, making aspect-level sentiment classification challenging. This study compares classical machine learning and ensemble methods for multilabel aspect-based sentiment analysis of amenities, attractions, and accessibility. The dataset comprises 13,085 Indonesian- and English-language Google Reviews labeled through a rule-based procedure. Reviews were represented using term frequency-inverse document frequency features and classified using logistic regression, multinomial naive Bayes, random forest, soft voting, adaptive boosting (AdaBoost), and stacking under a one-vs-rest framework. The stacking classifier combined logistic regression and random forest as base learners with logistic regression as the meta-learner. It achieved the best performance among the evaluated models, with a micro-F1 of 0.8626, macro-F1 of 0.7285, Hamming loss of 0.0633, and subset accuracy of 0.6825. These findings indicate that stacking effectively integrates complementary decision patterns and provides a reproducible framework for multilingual multilabel analysis of tourism reviews.

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Teguh Budi PrakosoDiponegoro University1
Eko ArifiantoDiponegoro University1
Krisan Aprian WidagdoDiponegoro University1
Kusworo AdiDiponegoro University1
Muhammad Basyier ArdimaDiponegoro University1
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Naretha Kawadha Pasemah GumayDiponegoro University1
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R. Rizal IsnantoDiponegoro University1
Arief ArfriandiState University of Semarang1
Indra WaspadaDiponegoro University1
Naufal Daffa PramudyaDiponegoro University1
Heri MulyantiDiponegoro University1
Istadi IstadiDiponegoro University1
Iwan SetiawanDiponegoro University1
Winda PratiwiDiponegoro University1
Retno KusumaningrumDiponegoro University1
Hilman Singgih WicaksanaDiponegoro University1
Sorja KoesumaSebelas Maret University1
Muhammad Irham NurwidyantoDiponegoro University1
Udi HarmokoDiponegoro University1
Gregrius Alvin FernandoDiponegoro University1
Zaenal ArifinDiponegoro University1
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Stacking ensemble for multilingual aspect-based sentiment analysis of tourism reviews</dc:title>
	<dc:creator>Pramono, Basworo Ardi</dc:creator>
	<dc:creator>Gernowo, Rahmat</dc:creator>
	<dc:creator>Sofwan, Aghus</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Amenities, attractions, accessibility; Aspect-based sentiment analysis; Multilabel classification; Multilingual tourism reviews; Stacking ensemble</dc:subject>
	<dc:description xml:lang="en-US">Multilingual tourism reviews often express opinions about multiple destination attributes within a single text, making aspect-level sentiment classification challenging. This study compares classical machine learning and ensemble methods for multilabel aspect-based sentiment analysis of amenities, attractions, and accessibility. The dataset comprises 13,085 Indonesian- and English-language Google Reviews labeled through a rule-based procedure. Reviews were represented using term frequency-inverse document frequency features and classified using logistic regression, multinomial naive Bayes, random forest, soft voting, adaptive boosting (AdaBoost), and stacking under a one-vs-rest framework. The stacking classifier combined logistic regression and random forest as base learners with logistic regression as the meta-learner. It achieved the best performance among the evaluated models, with a micro-F1 of 0.8626, macro-F1 of 0.7285, Hamming loss of 0.0633, and subset accuracy of 0.6825. These findings indicate that stacking effectively integrates complementary decision patterns and provides a reproducible framework for multilingual multilabel analysis of tourism reviews.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-10-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/29326</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i5.pp4381-4391</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 5: October 2026; 4381-4391</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i5</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/29326/15404</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Basworo Ardi Pramono, Rahmat Gernowo, Aghus Sofwan</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>