## 5 Experimental Results and Evaluation
### Results Analysis
However, around the fifteenth epoch a shift is observed in which the loss value initiates an upward trajectory, eventually plateauing; notably, our QGDA model showcases a marked advantage in its loss curve on both the training and validation sets when contrasted with other baseline models. In reference to the phenomenon of the validation set's loss being lower than that of the training set, it is posited that this disparity arises due to the regularization term exclusively employed during training. This augmentation of the  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**QGDA**} & \multicolumn{2}{c}{**FISHQDA**} & \multicolumn{2}{c}{**BQGA**} & \multicolumn{2}{c}{**BERT-Att**} & \multicolumn{2}{c}{**FISHQA**} & \multicolumn{2}{c}{**HAN**} \\ \cline{2-13}  & **Prec. ** & **Recall** & **Prec. ** & **Recall** & **Prec. ** & **Recall** & **Prec. ** & **Recall** & **Prec. ** & **Recall** & **Prec. ** & **Recall** \\ \hline Buying & 0.763 & 0.784 & 0.724 & 0.823 & 0.674 & 0.817 & 0.710 & 0.769 & 0.743 & 0.790 & 0.659 & 0.820 \\ Selling & 0.900 & 0.862 & 0.927 &  
training set's loss is not mirrored during the validation or prediction phases, leading to a reduction in loss compared to that of the training set. A series of ablation experiments were also conducted to investigate the impact of different components of our model. First, the performance of BERT-Attention was evaluated, which achieved an F1-score of 70.61%, almost equal to that of HAN. This suggests that simply using BERT to replace the word encoder is not highly effective. We next evaluated the performance of BQGA, which not only employs BERT but also incorporates a query set in the sentence-level attention mechanism; it achieved an F1-score of 71.51%, outperforming BERT-Attention and FISHQA by 0.9% and 0.14%, respectively. Similar to the comparison between FISHQA and HAN, this result demonstrates the effectiveness of our query set-based attention mechanism. Subsequently, a comparison was made with FISHDQA, consisting of FISHQA with a dual attention mechanism similar to that of our proposed QGDA model. It achieved an F1-score of 73.16%, significantly outperforming the original FISHQA by 1.79% and BQGA by 1.65%. This suggests that the dual mechanism between different queries improves model performance even more than the use of BERT in the word encoder. However, the performance of FISHDQA remained lower than that of our QGDA model by 2.3%. It is hypothesized that this discrepancy stems from the enhanced representativeness of the sentence vectors produced by BERT when used in conjunction with our query-based dual attention mechanism within the sentence encoder. This is further corroborated by comparison to BQGA, which lacks the dual attention mechanism; in this comparison, our QGDA model shows a performance improvement of 3.96%. This significant enhancement underlines the efficacy of the query-driven attention mechanism, which when coupled with our novel dual attention framework systematically guides the model's focus across different interpretive query perspectives. By assigning varying degrees of attention to these queries, the model can leverage the most relevant aspects for each category, thereby enriching the explainability and accuracy of the predictions. Our detailed analysis reveals that this selective attention not only facilitates a deeper understanding of the factors influencing stock fluctuations but also substantially boosts the predictive power of our QGDA model, highlighting the critical role of targeted query-specific attention in complex sentiment analysis tasks.