Semantic sparse attention-based algorithm for sentiment analysis

Financial news serves as a primary conduit for conveying dynamics in financial markets and changes in economic policies. Fully capturing the information in financial news benefits investors, corporations, and governments in understanding and responding to market shifts. This paper addresses challenges encountered in the sentiment analysis of financial news, including slow model convergence and indirect transmission of long-distance dependent information, by proposing a novel deep learning model designed to effectively capture contextual information in financial news texts. The model incorporates a semantic attention mechanism to preliminarily extract semantic information from the text and learn the correlations between words, thereby enhancing model convergence speed. Considering that the model might lead to overly dispersed attention weights and hinder effective focus on crucial local information when processing long sequences, we introduce a sparse attention module capable of efficiently modeling local dependency relations. Experimental validation on a financial news dataset demonstrates that our model outperforms traditional methods in sentiment analysis tasks in terms of accuracy and generalization ability, confirming the effectiveness of the semantic attention mechanism and the sparse attention module.