For imbalance in the number of topics and documents, C-LDA mimics document over-sampling in the Gibbs sampler using a different unit-value in the word count table for each collection.
a unit η c is chosen for each collection such that the average equivalent number of assigned words per-topic This process both increases the topic quality (in terms of collection balance) in the resulting heldout perplexity of the model.