Aspect-Based Sentiment Analysis for Thai Language from Consumer Reviews Towards Smartphone
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Abstract
The purpose of this research is to develop a model for aspect-based sentiment analysis for Thai language from consumer reviews towards smartphone which consists of the camera, battery, screen, performance, and price aspects that collected from YouTube with number of 67,907 comments including 6 brands: Apple, Samsung, Xiaomi, Vivo, Oppo, and Huawei. The process consists of: 1) Data collection 2) Data preprocessing 3) Aspect-based sentiment analysis, and 4) Model performance evaluation. This research used machine learning model and deep learning model to compare performance sentiment classification and performance evaluation from precision, recall, F-measure, and accuracy metric. The result suggests WangchanBERTa model is the most reliable method for sentiment classification.
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