  Aspect-Opinion Pair Extraction (AOPE) from Chinese financial texts is a
specialized task in fine-grained text sentiment analysis. The main objective is
to extract aspect terms and opinion terms simultaneously from a diverse range
of financial texts. Previous studies have mainly focused on developing grid
annotation schemes within grid-based models to facilitate this extraction
process. However, these methods often rely on character-level (token-level)
feature encoding, which may overlook the logical relationships between Chinese
characters within words. To address this limitation, we propose a novel method
called Graph-based Character-level Grid Tagging Scheme (GCGTS). The GCGTS
method explicitly incorporates syntactic structure using Graph Convolutional
Networks (GCN) and unifies the encoding of characters within the same syntactic
semantic unit (Chinese word level). Additionally, we introduce an image
convolutional structure into the grid model to better capture the local
relationships between characters within evaluation units. This innovative
structure reduces the excessive reliance on pre-trained language models and
emphasizes the modeling of structure and local relationships, thereby improving
the performance of the model on Chinese financial texts. Through comparative
experiments with advanced models such as Synchronous Double-channel Recurrent
Network (SDRN) and Grid Tagging Scheme (GTS), the proposed GCGTS model
demonstrates significant improvements in performance.
