Topic Analysis

Topic analysis aims to uncover underlying themes and patterns within large collections of text data, facilitating insights into diverse phenomena. Current research emphasizes the development of sophisticated models, including those based on Latent Dirichlet Allocation (LDA), BERT embeddings, and Graph Isomorphism Networks, often incorporating multimodal data (e.g., images and text) and leveraging large language models (LLMs) for improved accuracy and interpretability. These advancements are proving valuable across numerous fields, from predicting insurance claims and classifying online toxicity to enhancing information retrieval and understanding public sentiment surrounding major events. The resulting insights have significant implications for various applications, including social science research, public health initiatives, and the development of more effective AI systems.