Ontology-Driven Emotion Multi-Class Classification and Influence Analysis of User Opinions on Online Travel Agency

The rise in social media has transformed Online Travel Agencies (OTAs) into platforms where users actively share their experiences and opinions. However, conventional opinion mining approaches often fail to capture nuanced emotional expressions or connect them to user influence. To address this gap, this study introduces an ontology-driven opinion mining framework that integrates multi-class emotion classification, aspect-based analysis, and influence modeling using Indonesian-language discussions from the social media platform X. The framework combines an OTA-specific ontology that formally represents service aspects such as booking support, financial, platform experience, and event with fine-tuned IndoBERT for emotion recognition and sentiment polarity detection, and Social Network Analysis (SNA) enhanced by entropy weighting and TOPSIS to quantify and rank user influence. The results show that the fine-tuned IndoBERT performs strongly with respect to identification and sentiment polarity detection, with moderate results for multi-class emotion classification. Emotion labels enrich the ontology by linking user opinions to their affective context, enabling the deeper interpretation of customer experiences and service-related issues. The influence analysis further reveals that structural network properties, particularly betweenness, closeness, and eigenvector centrality, serve as the primary determinants of user influence, while engagement indicators act as discriminative amplifiers that highlight users whose content attains high visibility. Overall, the proposed framework offers a comprehensive and interpretable approach to understanding public perception in Indonesian-language OTA discussions. It advances opinion mining for low-resource languages by bridging semantic ontology modeling, emotional understanding, and influence analysis, while providing practical insights for OTAs to enhance service responsiveness, manage emotional engagement, and strengthen digital communication strategies.

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Publication Details

Journal
Future Internet
Published
2025-12-17
DOI
https://doi.org/10.3390/fi17120582
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
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article

Ontology-Driven Emotion Multi-Class Classification and Influence Analysis of User Opinions on Online Travel Agency

Hanif Fakhrurroja, Muharman Lubis, Putri Utami Rukmana, Asriana et al.
Future Internet
Sentiment Analysis and Opinion Mining
article

Ontology-Driven Emotion Multi-Class Classification and Influence Analysis of User Opinions on Online Travel Agency

Hanif Fakhrurroja, Muharman Lubis, Putri Utami Rukmana, Asriana, Alif Noorachmad Muttaqin
article en

Abstract

The rise in social media has transformed Online Travel Agencies (OTAs) into platforms where users actively share their experiences and opinions. However, conventional opinion mining approaches often fail to capture nuanced emotional expressions or connect them to user influence. To address this gap, this study introduces an ontology-driven opinion mining framework that integrates multi-class emotion classification, aspect-based analysis, and influence modeling using Indonesian-language discussions from the social media platform X. The framework combines an OTA-specific ontology that formally represents service aspects such as booking support, financial, platform experience, and event with fine-tuned IndoBERT for emotion recognition and sentiment polarity detection, and Social Network Analysis (SNA) enhanced by entropy weighting and TOPSIS to quantify and rank user influence. The results show that the fine-tuned IndoBERT performs strongly with respect to identification and sentiment polarity detection, with moderate results for multi-class emotion classification. Emotion labels enrich the ontology by linking user opinions to their affective context, enabling the deeper interpretation of customer experiences and service-related issues. The influence analysis further reveals that structural network properties, particularly betweenness, closeness, and eigenvector centrality, serve as the primary determinants of user influence, while engagement indicators act as discriminative amplifiers that highlight users whose content attains high visibility. Overall, the proposed framework offers a comprehensive and interpretable approach to understanding public perception in Indonesian-language OTA discussions. It advances opinion mining for low-resource languages by bridging semantic ontology modeling, emotional understanding, and influence analysis, while providing practical insights for OTAs to enhance service responsiveness, manage emotional engagement, and strengthen digital communication strategies.

Future InternetVol. 17(12)
National Nuclear Energy Agency of Indonesia (ID), Telkom University (ID)
Openalex Percentile: Top 78%
Sentiment Analysis and Opinion Mining
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