The evolving international situation in Northeast Asia is associated closely with China's development. The construction of a sentiment information knowledge graph for this region enables the effective monitoring of public-opinion hotspots. This not only guides the healthy development of public opinion and assists government decision-making but also prevents political marketing, thus enhancing national language competence and promoting harmonious and stable international relations. Named Entity Recognition(NER) is a key technology and core task in constructing knowledge graphs and has garnered extensive attention from researchers. This study uses real-time hot-sentiment texts related to Northeast Asia from social media and portal websites as data sources. Considering the regional characteristics and geopolitical structure of Northeast Asia, a fine-grained NER dataset comprising 10 major categories and 35 subcategories is established. Furthermore, a sentiment entity-recognition model based on the pretrained language model RoBERTa and a multilayer residual BiLSTM-CRF architecture (RoBERTa-ResBiLSTM-CRF) is proposed. After the model completes label prediction, a post-processing strategy based on rule templates is designed to improve the overall entity-recognition performance. Experimental results demonstrate that the proposed sentiment NER model outperforms the mainstream neural-network models, thus validating the effectiveness of the approach
