## 5 Experimental Results and Evaluation
### Effect of Explainable Basis
Meanwhile, the mean accuracy resulting from the 100 replications of random sampling classification closely mirrored the overall dataset mean. Furthermore, the result of each random sampling classification, represented by the colored dots in the graph, indicates that the disparities in accuracy among the 100 random classifications are significantly narrower than the classification effects achieved by the QGDA mechanism of our model. This graphical representation reinforces the robustness and distinctiveness of our model's concept-level explainable basis. The statistical validation of the explainability basis provided by different dominant query categories on the predictive performance is demonstrated in Table 4. One-way ANOVA was utilized to assess the variance in performance across these query-driven categories. The significant _p_-value of 0.0368 from ANOVA suggests non-uniformity in performance, indicating that certain query categories significantly influence predictive accuracy when identified as the dominant driver by QGDA. Subsequent _t_-tests comparing the accuracy of each QGDA-identified dominant query group against the mean accuracy from random sampling revealed a marked difference, with all categories except Industry Analysis showing statistically significant improvements or differences. This evidence suggests that the concept-level explainable basis generated through QGDA's query attention contributes substantially to identifying articles with predictions that are more consistent with market movements, with each category offering distinctive insights that deviate from random baseline expectations. Even though the experimental results may be influenced by the size and type of the dataset, the evident discrepancies in accuracy among different dominant query categories allow us to infer that the empirical evidence substantiates the reliability of our query-based QGDA mechanism as a solid foundation for concept-level explainability. In the realm of financial analysis, interpreting the QGDA query attention weights \(v\) as indicative of an article's main analytical perspective proves to be highly valuable. For \begin{table}
\begin{tabular}{l c c} \hline \hline
**Category** & _p_**-Value (ANOVA)** & _p_**-Value (t-test)** \\ \hline Macroeconomic Analysis & 0.0368 & \textless{}0.0001 \\ Industry Analysis & 0.0368 & 0.1390 \\ Business Analysis & 0.0368 & \textless{}0.0001 \\ Financial Analysis & 0.0368 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical analysis of stock fluctuation predictive performance across different highest attention weight queries. Figure 4: Comparison of stock fluctuation prediction accuracy across different highest attention weight queries. instance, within our experimental framework, \(q_{4}\) is indicative of a financial perspective, whereas \(q_{1}\) aligns with macroeconomic. As derived from the outcomes of our quantitative analysis, it can be reasonably concluded that utilizing our QGDA model to classify stock review articles and identify the dominant analytical perspective (via query attention) significantly enhances the reliability of analyses grounded in specific financial perspectives (such as \(q_{4}\)) over those based on macroeconomic perspectives (such as \(q_{1}\)), as demonstrated by the distribution of QGDA's query attention weights. Our proposed model endowed with this validated concept-level explainable basis makes a unique contribution to financial sentiment analysis. The practical utility of this explainability is multi-faceted:  
* For financial analysts, it allows for rapid verification of a prediction's rationale. An analyst can see whether the model's decision was based on relevant financial concepts (e.g., 'profit margin', 'debt ratio') or spurious correlations, thereby building trust and facilitating human-in-the-loop validation. * For model auditing and debugging, it provides a transparent lens to diagnose model failures.