SA-Model: Multi-feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing, ancient literature research, etc. However, the existing research on sentiment analysis is relatively small and does not effectively solve the problems such as the weak feature extraction ability of poetry text, which leads to the low performance of the model on sentiment analysis for Chinese classical poetry. In this research, we offer the SA-Model, a poetry sentiment analysis model. SA-Model firstly extracts text vector information and fuses it through BERT-wwm-ext and ERNIE models to enrich text vector information; Secondly, it incorporates numerous encoders to extract text features at multiple levels, thereby enriching text feature information, improving text semantics accuracy, and enhancing the model's learning and generalization capabilities; finally, multi-feature fusion poetry sentiment analysis model is constructed. The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus. Compared with other baseline models, the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.