## 5 Experimental Results and Evaluation
### Results Analysis  
In this study, we rigorously evaluated the sentiment classification performance of our novel hierarchical BERT-GRU model with Query-Guided Dual Attention (QGDA) in comparison to several baseline methods. To ensure a robust evaluation, we utilized a 7:1:2 training-validation-testing split for our datasets. The outcomes of our experimental analysis on the test set are thoroughly documented in Table 2. This table provides a detailed performance comparison of QGDA against other baseline models, highlighting our model's superior accuracy and efficiency in sentiment classification. Additionally, Table 3 offers a class-wise performance analysis, which helps in understanding the effectiveness of our model across different sentiment categories. From Table 2, it can be observed that our QGDA model achieved an F1-score of 75.38%. While this represents a modest 2.3% improvement over the next-best baseline, its primary significance lies not in the marginal gain itself but in demonstrating that our model achieves state-of-the-art performance while simultaneously introducing a crucial layer of concept-level explainability. The performance gain indicates that guiding the model with an explicit financial concept-based query set is not only effective for interpretability but also contributes positively to classification accuracy. This result validates our core thesis, namely, that integrating domain-specific verifiable explanations does not come at the cost of performance, and can in fact enhance it. To validate the model's performance, we conducted statistical tests comparing the proposed QGDA model against each baseline model. The one-sample \(t\)-tests yielded \(p\)-values significantly below the 0.05 threshold, indicating that the performance improvements of the QGDA model are statistically significant for both the accuracy and F1-score metrics. The consistency of these \(p\)-values across comparisons underscores the robustness of our model's enhancements. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Accuracy** & **F1-Score** \\ \hline HAN & 70.44 & 70.48 \\ FISHQA & 71.32 & 71.37 \\ BERT-Attention & 70.63 & 70.61 \\ BQGA & 71.34 & 71.51 \\ FISHDQA & 73.45 & 73.16 \\ Our Model (QGDA) & 75.38 & 75.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall sentiment analysis performance comparison of baseline and proposed models. Table 3 presents the detailed precision and recall results for each sentiment category across all models, allowing more granular performance characteristics to be observed. Our QGDA model generally maintains strong performance across categories, exhibiting notable recall in the \"Following\" (0.817) and \"Others\" (0.679) classes, demonstrating its proficiency in capturing nuanced sentiments within these diverse categories. It is interesting to note that while FISHQDA shows strong recall for \"Buying\" (0.823) and FISHQA for \"Selling\" (0.897), our QGDA model still achieves a balance of high precision and recall across all classes. This indicates QGDA's robustness and comprehensive understanding of various sentiment expressions, which collectively contributes to its superior overall F1-score, as shown in Table 2. The disaggregated metrics in Table 3 provide insights into the strengths and weaknesses of each model at a finer level of granularity, reinforcing QGDA's leading performance across the board. Another valuable insight can be garnered through the observation of the loss value's progression curve. The training of all models was configured to span 50 epochs. Figure 2, shows how the loss curves of various models evolved during the iterative training process. At the outset of each model's training, the loss value exhibits a continuous decline accompanied by a simultaneous enhancement in the model's accuracy on the validation set.