## 1 Introduction
This approach not only refines attention from word and sentence levels but also extends it to the query set itself, introducing a novel way to achieve concept-level explainability for document-level sentiment analysis in the stock market. Each specific query includes a list of financial terms and embodies a particular perspective, that is, a distinct financial concept, which analysts can use when evaluating stock market-related documents. In practical applications, deploying our BERT-GRU model with QGDA as a classifier demonstrates a notable advantage over conventional sentiment analysis models tailored to the financial domain. Rather than merely predicting the sentiment polarity within stock-related articles, our model goes a step further: by examining the attention distribution across the domain-specific query set, it illuminates the query category representing a particular financial concept or driver, which holds significant influence during classification. Consequently, this approach allows for a deeper understanding of the basis of text classification models during decision-making, specifically the perspective from which sentiment classification judgments are made, thereby providing a more valuable and interpretable foundation for analysts in the financial domain. Compared to prior deep learning approaches, our study not only enhances classification performance for financial domain texts by integrating BERT with our Query-Guided Dual Attention mechanism, it also provides a significant leap in structured, domain-specific explainability. This strategic incorporation fills a critical gap in financial sentiment analysis, where existing models often excel in performance but lack in transparently explaining their decision-making process. By focusing on both the content of the financial texts and the queries defined by financial experts, our QGDA mechanism offers an innovative route to more comprehensively understanding the underlying sentiments and their drivers in financial documents. To rigorously verify the reliability of the explanatory basis offered by the QGDA mechanism, a quantitative experiment was further devised, as detailed in the subsection titled \"Effect of Explainable Basis\". Crucially, this validation framework assesses whether the model's explanations correlate with real-world outcomes, specifically the dominant query categories identified by QGDA for each document. Our findings demonstrate that predictions guided by these dominant conceptual drivers exhibit statistically significant higher consistency with actual stock market fluctuations. This quantitative exploration strengthens our model's position by empirically demonstrating its ability to offer not just superior classification accuracy but also actionable and validated insights into the decision-making process, providing a dual benefit that is especially crucial in the field of financial sentiment analysis. The first level of our model fine-tunes the pretrained BERT model to capture the connections between words and learn the features of sentences that are split from documents. In the second level, we construct a GRU [7] model with the novel QGDA mechanism to aggregate the sentence representations obtained from BERT into document representations. The QGDA mechanism incorporates the customized knowledge-driven query set into the attention mechanism's calculation process as derived from the transformer [8]. Through the distribution of attention weight on queries, our model is able to provide explainable evidence of which kind of query plays the main role in the document classification model, allowing it to represent specific financial concepts. The weights across the different sentences are assigned by the first layer of the QGDA mechanism. Rather than self-attention, attention weights derived from the deployment of diverse financial knowledge-based query sets can help the model pay more attention to the sentences connected to these queries, which are more indicative of financial analyst opinions.