Abstract: Financial sentiment analysis is a technology to judge the sentiment orientation of financial texts, which is widely used in public opinion analysis and regulatory coordination. Because financial texts contain implicit sentiment information, it is difficult to directly determine the sentiment polarity according to sentiment features. To address this problem, a financial implicit sentiment analysis model based on sentiment enhancement and semantic dependency (FSED) is proposed to improve the accuracy of classification. Firstly, FinBERT is used to generate word vectors, which are then input into Bi-GRU to extract contextual semantic information. A dual-polarity attention mechanism is constructed by embedding positive and negative sentiment word vectors to extract sentiment feature vectors in two contexts. Then, based on semantic dependency graph of the text, dependency relationships and relationship type matrix are established. By combining these two matrices with the top-k strategy, a selection attention matrix is constructed. This matrix is then input into the graph convolutional network to extract semantic dependency features of the text. Finally, the features from sentiment enhancement and semantic dependency are fused, and compressed using average pooling and max pooling layers. After that, the features are input into fully connected layers and Softmax to obtain the classification results. Experimental results show that compared with A-GCN, FSED can improve the accuracy of implicit sentiment analysis in the financial field.

Key words: financial implicit sentiment analysis, FinBERT, dual-polarity attention mechanism, semantic dependency graph, selection attention matrix