Transformer-Based Sentiment Classification for Innovative Customer Feedback Analysis in Thai Cosmetic Industry

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dc.contributor.authorTantiathimongkhon, Theerawut
dc.contributor.authorLimpisiri, Tanintorn
dc.contributor.authorSaengpan, Thanakit
dc.date.accessioned2026-08-06T10:53:34Z
dc.date.available2026-08-06T10:53:34Z
dc.date.issued2026-01-01
dc.description.abstractThis 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.
dc.identifier.citationInternational Conference on Cybernetics and Innovations Icci 2026, 2026
dc.identifier.doi10.1109/ICCI68752.2026.11506442
dc.identifier.other2-s2.0-105041330103
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17599
dc.sourceInternational Conference on Cybernetics and Innovations Icci 2026
dc.subjectCosmetic industry
dc.subjectMachine learning
dc.subjectSentiment analysis
dc.subjectSmall enterprises
dc.subjectThai language
dc.subjectWangchanBERTa
dc.titleTransformer-Based Sentiment Classification for Innovative Customer Feedback Analysis in Thai Cosmetic Industry
dc.typeConference Paper

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