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Access type: UnknownSubject: search.filters.subject.Aspect-Based Sentiment Analysis

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    Aspect-Level Sentiment Analysis Using WangchanBERTa for Fine-Grained Service Insight Extraction in Hotel Reviews
    (2026-06-01)
    Suwan, Thanachok
    ;
    Nokkaew, Manussawee
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    Surawanitkun, Chayada
    ;
    Sorn-In, Kanda
    ;
    Mueanrit, Nongram
    Online booking site reviews substantially influence Thai SME hotel reputations and consumer decisions. Hotels should readily extract useful information from unstructured Thai-language ratings. WangchanBERTa, a Thai deep learning model, automates hotel sentiment analysis and strategic insight development in this study. System is two-stage. Phase 1 divides 10,040 Thai hotel reviews from Agoda, Booking.com, Traveloka, and Trip.com into good and negative attitudes and determines price, service quality, and cleanliness. Phase 2 extracts aspect-level information across 11 service characteristics to discover complex trends like consumers being satisfied with service but unhappy with cost. The sentiment categorization model performed well with 91.63% accuracy and 89.69% macro F1-score in experiments. The aspect-based sentiment analysis system achieved 91.63% accuracy, 91.55% macro precision, 91.63% recall, 91.52% F1-score, and real-world insight extraction. This methodology helps hoteliers listen to customers, integrate data into business ideas, and compete in Thailand’s tourism market.
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    Application of Large Language Models for Aspect-Based Sentiment Analysis on Social Media Data: A Case Study of the Thai Telecommunications Industry
    (2026-01-01)
    Limseesawan, Krittapas
    ;
    Netisopakul, Ponrudee
    ;
    Chotipant, Supannada
    ;
    Voravuthikunchai, Winn
    ;
    Sirivorachodphokin, Thitirat
    Social media is a valuable source of consumer opinion data, particularly in Thailand's highly competitive telecommunications market among AIS, TRUE, and DTAC. This study compares three LLM-based approaches for Aspect-Based Sentiment Analysis (ABSA) on 3,508 Thai-language messages from Pantip.com and YouTube.com: (1) Prompt Engineering, (2) Domain Adaptation & Fine-Tuning, and (3) Multi-Agent Debate Framework. Results show that Domain Adaptation & Fine-Tuning achieves the best accuracy-latency trade-off, with Qwen2.5-1.5B-Instruct exceeding 87% average accuracy in under 4 seconds per message. Multi-Agent Debate achieves the highest Category accuracy (82.82%) but at a latency cost of 10-16 seconds per message. Gemini-2.5-Flash-Lite provides the best speed-accuracy balance for Prompt Engineering without additional training. Critically, small open-source models (1B-1.5B parameters) subjected to domain-specific fine-tuning can approach or surpass proprietary large models on this task, suggesting domain alignment may outweigh raw parameter scale for narrow, well-defined ABSA tasks.
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