Topic Model

Topic modeling is a machine learning technique used to discover underlying themes (topics) within collections of documents. Current research focuses on improving topic coherence and interpretability, particularly for short texts, by incorporating techniques like large language models (LLMs) for context expansion and refinement, and employing neural architectures such as variational autoencoders and graph isomorphism networks. These advancements enhance the accuracy and efficiency of topic discovery, with applications ranging from legal analysis and social media monitoring to historical research and clinical data analysis.