SemaTopic: A Framework for Semantic-Adaptive Probabilistic Topic Modeling

Abstract

Topic modeling is a crucial technique for Natural Language Processing (NLP) which helps to automatically uncover coherent topics from large-scale text corpora. Yet, classic methods tend to suffer from poor semantic depth and topic coherence. In this regard, we present here a new approach “SemaTopic” to improve the quality and interpretability of discovered topics. By exploiting semantic understanding and stronger clustering dynamics, our approach results in a more continuous, finer and more stable representation of the topics. Experimental results demonstrate that SemaTopic achieves a relative gain of +6.2% in semantic coherence compared to BERTopic on the 20 Newsgroups dataset (Cv=0.5315 vs. 0.5004), while maintaining stable performance across heterogeneous and multilingual corpora. These findings highlight “SemaTopic” as a scalable and reliable solution for practical text mining and knowledge discovery.