In the era of deep learning, the opaque nature of sophisticated models often stands at odds with the growing demand for transparency and explainability in Artificial Intelligence. This paper introduces a novel approach to text classification that emphasizes explainability without significantly compromising performance. We propose a modular framework to distill and aggregate information in a manner conducive to human interpretation. At the core of our methodology is the premise that features extracted at the finest granularity are inherently explainable and reliable; compared with methods whose explanation is on word-level importance, this layered aggregation of low-level features allows us to trace a clearer decision trail of the model’s decision-making process. Our results demonstrate this approach yields effective explanations with a marginal reduction in accuracy, presenting a compelling trade-off for applications where understandability is paramount.