I'm wondering how RNN can be used when doing sentiment analysis.
It seems that the characteristic of RNN is to remember what appeared in the past and determine the value of the present (future), but I think this RNN's characteristic is useless when analyzing sentiment.
I am curious to see how it is possible to do emotional analysis by remembering the words that have appeared so far.
I think the way to train vectors that show how many times specific words appear is the only way to analyze sentiment.
If some RNN model train a sentence "good movie" and label "positive", can it predict a new sentence "movie good" as a "positive"?
Also, if a normal model(just fully connected deep neural network) train a sentence "good movie" and label "positive", is it hard for this model to predict a new sentence "movie good" as a "positive"?