<p>Emotion recognition in texts is an important problem in modern natural language processing, currently dominated by transformer architectures. However, their internal mechanisms remain a black box, and classification quality — especially in complex cases — still has room for improvement. This paper proposes a novel hybrid approach that combines the capabilities of modern language models with a deep analysis of their vector representations by adapting the classical statistical pattern recognition method based on decomposition in a space with a generating element (Kunchenko space). The method produces a new set of statistical-geometric features derived from the reconstruction error of vector representations of text messages belonging to the corresponding classes. Experiments conducted on Ukrainian (EMOBENCH-UA) and English (EmoEvent) datasets demonstrate that the proposed hybrid approach yields a statistically significant improvement in classification accuracy. The study also identifies key conditions for the method’s effectiveness: it acts as a powerful refinement mechanism for models fine-tuned on the target task. However, the method is ineffective when applied to raw, non-specialized vector representations. Furthermore, the results indicate that the choice of basis functions for reconstruction is a crucial hyperparameter. This fact allows for adapting the method to the specific geometry of the data space.</p>