## 5 Experimental Results and Evaluation
### Effect of Explainable Basis  
The performance of our proposed model on this dataset demonstrates the effectiveness of queries and a dual attention mechanism at the sentence level. To answer RQ 3, we conducted several case studies to validate the explainability of our QGDA model. We focused on the query category attention weights, which are integral to the dual attention mechanism. By analyzing how different queries influence the attention mechanism, it is possible trace the reasoning behind the model's sentiment classification decisions. To begin, we compare the attention weight distribution of our proposed QGDA model with that of HAN and FISHQA on the same article. As previous research has shown that the explainability of attention distributions is sometimes ambiguous [51] and can be deceived [46], we refrained from analyzing the distribution of attention weights at the word-level. Instead, for greater reliability we focused solely on comparing the distribution of attention weights at the sentence level. Figure 3 visualizes the attention weights between sentences. As indicated in the figure caption, the depth of color in each sentence block directly represents its assigned attention weight, with darker shades signifying higher importance to the model's prediction. Our QGDA model stands out by virtue of its utilization of a dual attention mechanism that distinctly differentiates itself based on the queries. In contrast to FISHQA, which  
employs multiple queries to guide the attention mechanism and generates multiple query attention weights within the same sentence, our distinctive approach yields a singular and combined query-guided attention weight (\(\vec{a}\)) that steers the attention mechanism across different sentences. In this example article, our QGDA model clearly assigns high attention weights to the first and last sentence, effectively highlighting the most salient parts of the document for sentiment judgment. In comparison, the HAN model's attention weights are relatively average across sentences. Additionally, the distribution of attention weights between different queries in FISHQA appears less regular, which may complicate the interpretability of its attention mechanism at a granular level. However, from the perspective of financial analysis, the ability to clearly identify which sentences in a document (via our combined attention \(\vec{a}\)) or which underlying financial concepts (via query attention \(v\)) play a crucial role in the sentiment analysis model provides a direct and tangible degree of explainable basis. Even if it is difficult to quantify precisely, this qualitative insight into the model's reasoning is invaluable for financial professionals seeking to understand the 'why' behind a prediction. Thus, we propose a novel concept-level explainable basis founded on the query attention weights from our QGDA mechanism. This not only considers the attention weight between words and sentences, but more critically considers the attention given to different perspective queries representing financial concepts. Because the queries themselves play a crucial role in guiding the attention mechanism in document sentiment classification, the attention weights between different queries may provide an explainable basis that can illustrate the main analytical perspective influencing the article's sentiment prediction. Especially in stock-related review articles, the main perspectives in the analysis not only affect the credibility but also directly impact the accuracy of the analytical content. Thus, we conducted a case study on our dataset to analyze the price fluctuations of stocks covered in securities review articles. To further substantiate the explanatory power of our QGDA model, we devised an experiment to compare consistency between the stock price movement trends indicated by articles (where sentiment is predicted by our model) and the subsequent actual price fluctuations when predictions are grouped by the dominant financial concept (query category) identified by QGDA.