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Aspect-Opinion Pair Extraction (AOPE) from Chinese financial texts is aspecialized task in fine-grained text sentiment analysis. The main objective isto extract aspect terms and opinion terms simultaneously from a diverse rangeof financial texts. Previous studies have mainly focused on developing gridannotation schemes within grid-based models to facilitate this extractionprocess. However, these methods often rely on character-level (token-level)feature encoding, which may overlook the logical relationships between Chinesecharacters within words. To address this limitation, we propose a novel methodcalled Graph-based Character-level Grid Tagging Scheme (GCGTS). The GCGTSmethod explicitly incorporates syntactic structure using Graph ConvolutionalNetworks (GCN) and unifies the encoding of characters within the same syntacticsemantic unit (Chinese word level). Additionally, we introduce an imageconvolutional structure into the grid model to better capture the localrelationships between characters within evaluation units. This innovativestructure reduces the excessive reliance on pre-trained language models andemphasizes the modeling of structure and local relationships, thereby improvingthe performance of the model on Chinese financial texts. Through comparativeexperiments with advanced models such as Synchronous Double-channel RecurrentNetwork (SDRN) and Grid Tagging Scheme (GTS), the proposed GCGTS modeldemonstrates significant improvements in performance.

Chinese Financial Text Emotion Mining: GCGTS -- A Character Relationship-based Approach for Simultaneous Aspect-Opinion Pair Extraction