This work presents an initial approach to studying the explainability of deep learning models at the layer level using fuzzy logic techniques. The aim is to link neural activations to input text concepts, analyzing their coherence and relationships. For this, a dataset of movie reviews is used, where each text is classified into one of seven emotions via a sentiment analysis model. The resulting activation vectors are clustered, allowing each vector to belong to multiple clusters, thus reflecting the ambiguity of textual data. The resulting clusters are analyzed through coherence measures to assess their internal consistency and uncertainty. These analyses help reveal how similar concepts are grouped in the neural activations. Preliminary results are promising, motivating further exploration of this approach. As an additional contribution, this work proposes the application of the same methodology to a domain with more objective concepts, such as wine recommendation. In this case, fuzzy profiles are defined based on organoleptic attributes of wine, and affinity scores between wines and user profiles are computed using TSK FIS models. These affinity values are used as neural network training targets, allowing subsequent analysis of how the network organizes these clearer and more structured concepts in its hidden activations.