Seo-In Choi  Dae-Min Park  Byung-Won On

10.5626/JOK.2025.52.4.319

sentiment analysis Deep Learning BERT KoBERT KLUE mixture of experts (MoE)

News articles provide information on various topics such as politics, economics, society, and culture. Their neutral tone often limits the ability of traditional sentiment analysis models to effectively capture emotions. To address this issue, we proposed a novel sentiment analysis model that combined quotations with article attribute values. For sentiment analysis, we employed deep learning-based models such as BERT, KoBERT (optimized for Korean), and KLUE. Embedding results from these models were integrated using a Mixture of Experts (MoE) structure to simultaneously learn the emotional information in quotations and the attribute information of articles. Experimental results demonstrated that the proposed models, including the attribute-based phrase and attribute group embedding models, achieved higher accuracy and reliability than conventional quotation-only analysis and traditional machine learning models. In particular, the KLUE model optimized for Korean data showed improved performance. Incorporating diverse attribute information significantly enhanced predictive accuracies of sentiment analysis models. These findings suggest that effectively combining quotation data with article attribute information enables more sophisticated sentiment analysis, even for neutral news articles.