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I have two groups of texts that are very similar (e.g. reviews written on fridays and reviews written on mondays), and I want to build a LSTM that can classify them into positive and negative reviews.

The question is then how to input the categories (Monday/Sunday) such that the model also takes those into account.

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Options considered:

  1. Make two models (which I would rather not given the text similarities)
  2. Add a variable to the start of the tokenized texts (0 and 1 for the Friday/Monday). This will likely have very little impacts due to their small weight in the entire context of the LSTM.
  3. Like (2.) but give these variables a higher weight
  4. Just hope that the network finds out itself (should one balance the categories friday/monday and the categories positive/negative for each?).

It is quite simple in most other non-sequential models, as one e.g. with a bag-of-words and random forest just could force the dummy variable to be in all trees, or one could normalize the variables, such that the variable would have a weight just as high, as all the text combinedly.

Do you have any suggestions?

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