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Suppose we are doing sentiment analysis for a restaurant. Customers can rate the restaurant by #1: how expensive the restaurant is, #2:how good is the food and #3: how likely they will come again. The ratings are dependent,i.e. the more expensive the restaurant is (higher #1), the less likely they will come back (lower #3), but whey will if the food is good (higher #2).

My questions are: is there a good RNN structure(review as input, #1-#3 as output) that can capture and model the dependency among #1 - #3?

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  • $\begingroup$ "(review as input, #1-#3 as output) " you want one input and 3 different outputs, are you referring to multi-task learning? And what do you mean exactly with "dependency among #1 - #3"? Can't you just perform classic statistic like correlation between variables and stuff? $\endgroup$ – Edoardo Guerriero Mar 14 at 14:47
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Inspired by (1) machine translation where source phrases are first encoded to a feature vector and decoded by another RNN and (2) automatic captioning where a picture is also encoded to a feature vector before it's decoded by the RNN followed. A proper structure for the problem above may be the inverse of these, where the review is encoded by RNN to a vector, and a DNN is followed to decode the vector into #1 - #3.

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