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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
    ;
    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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    Transformer-Based Sentiment Classification for Innovative Customer Feedback Analysis in Thai Cosmetic Industry
    (2026-01-01)
    Tantiathimongkhon, Theerawut
    ;
    Limpisiri, Tanintorn
    ;
    Saengpan, Thanakit
    This research aims to develop an effective sentiment analysis system for classifying Thai cosmetic reviews, which present unique linguistic challenges such as the lack of word boundaries, the use of domain-specific terminology, and the prevalence of sarcasm in online discourse. We propose a hybrid approach that combines domain-specific pre-processing with machine learning and deep learning architectures, constructing a specialized beauty corpus and extracting sentiment-bearing keywords. We compare the performance of three models: Support Vector Machine (SVM), Bidirectional LSTM (Bi-LSTM), and fine-tuned WangchanBERTa. The experimental results demonstrate that WangchanBERTa significantly outperforms both traditional approaches, achieving an F1-score of 0.9450, compared to 0.8652 for SVM and 0.7600 for Bi-LSTM. Error analysis of the SVM model reveals specific challenges of the Thai language, such as complex negation usage and temporal sentiment shifts, which are effectively addressed by the Transformer-based architecture. The proposed system offers scalable solutions for both large e-commerce platforms and small enterprises, enabling smart customer feedback analysis that enhances business productivity and supports data-driven resource management. This study showcases the effectiveness of integrating domain-specific natural language processing techniques with pre-trained language models, providing a robust benchmark for sentiment analysis in the beauty industry and contributing to technological innovation and economic growth in Thailand's digital economy.
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    Thai Question-Answering System Using Similarity Search and LLM
    (2024-07-01)
    Jearanaitanakij, Kietikul
    ;
    Srithongdee, Chananchida
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    Ketkham, Sirinoot
    ;
    Ardsana, Onwanya
    ;
    Kullawan, Tiwat
    A question-answering (QA) system is essential to an organization where numerous QA pairs respond to customer queries. Choosing the right pair corresponding to the query is a complex task. Although the QA system from a commercial product like ChatGPT provides an excellent solution, it is costly, and the fine-tuned Large Language Model (LLM) cannot be downloaded for private use at the local site. In addition, the cost of using such LLM may significantly increase when the number of users grows. We propose a Thai QA system that can swiftly respond and correctly match the user query to the reference answer in the QA dataset. The proposed system encodes both QA pairs and a query into individual embeddings and finds a couple of QA pairs that are most related to the query by using the fast similarity search called Faiss (Facebook AI Similarity Search.) Afterward, the relevant QA pairs and the query are fed to the fine-tuned LLM (WangchanBERTa-pretraining multilingual transformer-based) to choose the single best match QA pair. The fine-tuned WangchanBERTa can retrieve the correct answer and respond to the query naturally. The experiment conducted on the Thai Wiki QA dataset indicates the superior ROUGE values, precision, recall, F1-score, and runtime of the proposed system against other strategies.