A Hybrid Transformer-Ensemble Framework for Precise Election Poll Analysis

The prediction of election outcomes is a critical area in political analysis and decision-making. Accurate forecasting models can significantly influence electoral strategies and policy formulation. Existing models, however, face challenges in handling complex, dynamic, and high-dimensional election data. This paper addresses these issues by utilizing the Election Polls Dataset, which includes structured and unstructured data from multiple polling agencies such as YouGov (U.K. and NY, USA), Morning Consult (Washington, DC, USA), and Harris Insights (Chicago, IL, USA). We propose a novel hybrid approach combining Transformer models with ensemble learning techniques, including Random Forest, XGBoost, and Gradient Boosting, to enhance prediction accuracy. The novelty of this approach lies in the integration of Transformer’s attention mechanism with ensemble methods, improving both prediction accuracy and model stability. The performance of the model is evaluated using metrics such as accuracy, F1-Score, precision, recall, and AUC-ROC. Experimental results show that the proposed model outperforms existing methods, achieving a 93.4% accuracy, surpassing the baseline model by 1.4%.