Normalize / Standardize in a Random Forest? If I have a matrix of co-occurring words in conversations of different lengths, is it appropriate to standardize / normalize the data prior to training? 

My matrix is set up as follows: one row per two-person conversation, and columns are the words that co-occur between speakers. I cannot help but think that, as a longer conversation will likely comprise more shared words than shorter ones, I should factor this in somehow.
Thanks for the clarification by commenting. Tree-based models do not care about the absolute value that a feature takes. They only care about the order of the values. 
Hence, normalization is used mainly in linear models/knn/neural networks because they're affected by absolute values taken by features.

You don't need to normalize/standardize.

Check this post.