Topic Evolution

Topic evolution research focuses on understanding how topics emerge, change, and interact over time within various text corpora, such as social media posts or scientific publications. Current research employs dynamic topic modeling and graph embedding techniques, often incorporating pre-trained language models to analyze evolving language features and identify lead-lag relationships between different datasets. This work is significant for improving text classification accuracy in dynamic environments, detecting emerging research trends, and providing time-aware insights into customer sentiment and market opportunities.