Dynamic Embedded Topic Models: properties and recommendations based on diverse corpora
This work provides practical recommendations for applied scholars using topic models on historical texts, but it is incremental as it focuses on optimizing an existing method.
The authors measured the impact of implementation choices for the Dynamic Embedded Topic Model across five diachronic corpora, finding that vocabulary scalability and flexible interval modeling are key for utility, while other aspects do not significantly affect performance.
We measure the effects of several implementation choices for the Dynamic Embedded Topic Model, as applied to five distinct diachronic corpora, with the goal of isolating important decisions for its use and further development. We identify priorities that will maximize utility in applied scholarship, including the practical scalability of vocabulary size to best exploit the strengths of embedded representations, and more flexible modeling of intervals to accommodate the uneven temporal distributions of historical writing. Of similar importance, we find performance is not significantly or consistently affected by several aspects that otherwise limit the model's application or might consume the resources of a grid search.