## 4 Results and Discussion
The depiction of sentiment analysis across the consecutive years enables tracking the long-term changes in customer perception and could provide invaluable insights for strategic planning or operational adjustments aimed at elevating the customer journey. Additionally, sensitivity analysis was conducted for the proposed prediction model of ticket fare to investigate the significance of various aspect groups. We utilized the aspects sentiment score dataset presented in Table 5, along with the flight fare dataset from Table 1. The model demonstrates exemplary performance when all aspects are considered, with a remarkably low RMSE = 0.0071, MAE = 0.0137, and a R\({}^{2}\) = 0.9899, indicative of its robust predictive power. When the aspect group of _safety and security_ is omitted, there is a noticeable decrease in model performance, as reflected by marked rises in RMSE 0.0672 and MAE 0.5431 and a substantial drop in the R\({}^{2}\) value to 0.6752. This suggests that the safety and security aspect group is critical for accurate fare forecasting. A similar trend is observed when the _staff_ aspect group is excluded, albeit to a lesser extent, with the RMSE increasing to 0.0482 and MAE to 0.6887 and the R\({}^{2}\) decreasing to 0.8142. Excluding _seat and cabin_ features results in an RMSE of 0.0412, MAE of 0.3128, and an R\({}^{2}\) of 0.9572, highlighting their significant but lesser influence compared to safety aspects. The exclusion of cleanliness  
Figure 8: Temporal distribution of positive and negative customer sentiments by aspects group. aspects still impacts the model but less pronouncedly, with RMSE = 0.0223, MAE = 0.1145, and R\({}^{2}\) = 0.9379, indicating a lower yet still noteworthy importance in fare prediction. Table 6 shows the impact of aspect group exclusion on prediction model performance. To further elucidate the impact of ABSA on the precision of predictive models, a comparative study was conducted. This study examined multiple models to determine the extent to which incorporating ABSA can refine flight ticket fare forecasting accuracy. Figure 9 illustrates the MAE for the MLP, LSTM, and GRU models, both with and without ABSA integration. It reveals that incorporating ABSA leads to performance enhancements across all models, with sequence-based models LSTM and GRU showing particularly significant improvements. Notably, the ABSA_GRU model recorded the lowest MAE at 0.0137, underscoring the efficacy of sentiment analysis in capturing critical price-related factors from passenger feedback. Further detailed in Figure 10 is an RMSE analysis over 1400 epochs, where the ABSA_GRU model consistently outperformed others, maintaining a low RMSE, indicative of stable data trend representation. In contrast, the MLP model exhibited a higher RMSE, highlighting the significant error reduction when ABSA is applied. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aspect Group Excluded** & **Change in RMSE** & **Change in MAE** & **Change in R\({}^{2}\)** \\ \hline Safety and Security & Increased by 0.0601 & Increased by 0.5294 & Reduced by 0.3147 \\ Staff & Increased by 0.0411 & Increased by 0.6750 & Reduced by 0.1757 \\ Seat and Cabin Features & Increased by 0.0341 & Increased by 0.2991 & Reduced by 0.0327 \\ Cleanliness & Increased by 0.0152 & Increased by 0.1008 & Reduced by 0.0520 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sensitivity analysis of the prediction model across customer service aspect groups. Figure 10: Comparative analysis of root mean square error (RMSE) across different model architectures over 1400 epochs. Figure 9: Mean absolute error (MAE) across different model architectures. An additional experiment assessed the impact of increasing the number of layers in a stacked GRU model, with results depicted in Figure 11. A clear trend emerged; RMSE decreases as more layers are added, achieving optimal performance with a seven-layer configuration. This suggests that additional layers enhance the model's predictive capabilities.