Can word embedding be used for text classification on a mix of English and non-English text?
In this case, I think it should be fine to use word embedding on both languages since word embedding learns the meaning of individual words regardless of languages...Is this reasonable? Or maybe I do need to separate the languages and build different models for each language?


  If I think logically you are correct. Word embedding is merely a
  collection of Tokens, which derived its features on the basis of
  nearby words in a sentence. So if you have sufficient raw data(mix of
  both), I think its good to go, though results will explain you more
  :).


However its good to see how such models will behave in case we have mix of LeftToRight(LTR) and RTL languages.