Latent Dirichlet Allocation

Latent Dirichlet Allocation (LDA) is a probabilistic topic modeling technique used to discover underlying thematic structures in large collections of text data. Current research focuses on improving LDA's performance, particularly for short texts and diverse domains, often by integrating it with other methods like large language models (LLMs), graph neural networks (GNNs), or transformer-based embeddings to enhance topic coherence and interpretability. LDA's applications span diverse fields, including healthcare, finance, social media analysis, and legal research, enabling automated extraction of insights from unstructured textual data and facilitating more efficient data analysis.