Combining heterogeneous data sets for more powerful machine learning
Following your considerations:


  IMDB and RT don't have all the same movies


I would build a larger dataset of movies, aggregate all observations available, and run a model on that.




  There are words local to each dataset. i.e. the word "rotten" might be
  a frequent word on RT, but never appears in the IMDB dataset.


I think this doesn't constitute a problem once you aggregate data in a larger dataset. At that point you can run some NLP method on the whole aggregated corpus. Let me know what you think.




  The outcome measures are different. RT uses a 5 star scale, whereas
  IMDB uses a 10 star one. How do we deal with this heterogeneity?


You just need to uniform the scales on a given interval like [0, 1]. Then all votes could be compared.