Dynamic Topic Model

Dynamic topic modeling (DTM) aims to uncover how topics emerge, evolve, and disappear within a collection of text documents ordered over time. Recent research emphasizes developing more sophisticated model architectures, including neural network-based approaches leveraging pre-trained language models and those incorporating techniques like contrastive learning and fractional Brownian motion to better capture topic interdependencies and long-term temporal dynamics. These advancements improve topic coherence, address issues like repetitive or unassociated topics, and enable more robust quantitative and qualitative evaluation. DTM finds applications in diverse fields, facilitating trend analysis, historical research, and the monitoring of evolving opinions or brand sentiment in large text corpora.