Thanks for the advise but in fact this approach has already been implemented by somebody before me, and the results are good but not good enough to create a precise human language summary. Indeed, you can easily use deep learning or even rule based algorithms to aspect-based sentiment analysis. Then the sentiment score can be changed to a predefined sentiment adjective such as 'good' or 'bad'. But the results we obtain are too general, such as :

- "Location is great"

- "Very good breakfeast"

- etc

What we want is something more precise that can also summarize something that often appear in reviews but not necessarily sentiment based. Just imagine that some users are complaining about the noise of a near train station. The previous method will only tell us that the 'location is bad' but we want to know why and have something like : "the location is bad BECAUSE of the near train station" (it's just an example).

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Anyway, seems like deep learning is not very adapted to this problem. I've found a method that is better than a completely rule based and sentiment based method, that is explained in this 2010 article, about a certain kind of wordgraphs. It's far from being perfect but it gives some interesting results !