In this study, we present our research focused on the development of innovative models designed to effectively and efficiently process textual information. We demonstrate that neural networks and fuzzy logic possess distinct characteristics that render them suitable for specific problem domains while presenting limitations in others. Neural networks excel in pattern recognition, but lack inherent interpretability for decision-making. Conversely, fuzzy logic systems offer interpretability, but lack the ability to automatically derive decision-making rules. These limitations have motivated the creation of an intelligent hybrid system, merging both techniques to address the aforementioned drawbacks at the individual level.