01891nas a2200193   4500008004100000245005900041210005900100260002200159300001400181520124700195653003001442653002501472653001901497653002701516100001701543700002101560700002001581856009601601       2025                        eng d00aHidden Neuron Activation Analysis on Labeled Text Data0 aHidden Neuron Activation Analysis on Labeled Text Data  aUSAbACMc12/2025  a206 - 2103 a<p>Understanding the internal mechanisms of deep neural networks remains a central challenge in the field of Explainable Artificial Intelligence (XAI). With the rapid advancement of neural architectures in natural language processing (NLP), analyzing the role of hidden neurons in capturing and processing linguistic features has become increasingly important. This study investigates Hidden Neuron Activation Analysis on labeled text data to reveal how individual neurons contribute to a model’s decision-making process. We propose a model-agnostic explainability framework for text classifiers that identifies concepts activating specific neurons involved in classification. An LSTM-based network is trained on the AG News topic classification dataset, comprising four distinct classes, and the final Dense layer with 64 neurons was analyzed. In addition, statistical analyses like the Mann-Whitney U Test is conducted to assess the robustness and reliability of the system. The statistical analysis shows that, concepts plays important role in the decision making process of neural network. Our findings enhance interpretability in NLP models and offer a foundation for optimizing neural architectures in text classification tasks.</p>
10aConcept-based Explanation10aDense Layer Analysis10aExplainable AI10aHidden Neuron Analysis1 aDas, Avishek1 aDalal, Abhilekha1 aHitzler, Pascal  uhttps://daselab.cs.ksu.edu/publications/hidden-neuron-activation-analysis-labeled-text-data