Research on Text Sentiment Recognition Algorithm Integrated Enneagram and BiGRU-attention-CNN

This study introduces a novel text sentiment analysis model that incorporates the enneagram personality theory into deep learning methodologies to address the oversight of traditional sentiment analysis tasks regarding the influence of individual personality traits on emotional expression. The model utilizes a bidirectional GRU, an attention mechanism, and a CNN for feature extraction, integrating these features with enneagram-based personality traits for predictive purposes. Experimental results showcase the high performance of the proposed model, particularly with the feature fusion technique based on residuals achieving accuracies of 91.4% and 90.6%, as well as F1 scores of 92.3% and 91.7% on the IMDB and MR datasets, respectively. Furthermore, the model exhibits strong generalization capabilities and robustness in practical scenarios.