Text Analytics

Text analytics uses computational methods to extract meaningful insights from textual data, aiming to understand patterns, trends, and sentiment within large volumes of unstructured information. Current research emphasizes the application of advanced techniques like topic modeling (e.g., Latent Dirichlet Allocation), large language models (LLMs), and various machine learning classifiers to address diverse challenges across fields such as public health, human resources, and public service. This rapidly evolving field is significantly impacting various sectors by enabling data-driven decision-making, improving service delivery, and facilitating a deeper understanding of complex social and behavioral phenomena.