Making sense of the black‐boxes: Toward interpretable text classification using deep learning models

Text classification is a common task in data science. Despite the superior performances of deep learning based models in various text classification tasks, their black‐box nature poses significant challenges for wide adoption. The knowledge‐to‐action framework emphasizes several principles concerning the application and use of knowledge, such as ease‐of‐use, customization, and feedback. With the guidance of the above principles and the properties of interpretable machine learning, we identify the design requirements for and propose an interpretable deep learning (IDeL) based framework for text classification models. IDeL comprises three main components: feature penetration, instance aggregation, and feature perturbation. We evaluate our implementation of the framework with two distinct case studies: fake news detection and social question categorization. The experiment results provide evidence for the efficacy of IDeL components in enhancing the interpretability of text classification models. Moreover, the findings are generalizable across binary and multi‐label, multi‐class classification problems. The proposed IDeL framework introduce a unique iField perspective for building trusted models in data science by improving the transparency and access to advanced black‐box models.