[D] How exactly does the LSTM allocate its weights and gates such that it can interpret sarcastic sentences?

In the following example, ""the movie sucks, said no one ever!", how exactly are the weights of the lstm allocated in order for the lstm to recognise that the movie sucks is a positive statement and the no one ever is also leading to positive sentiment.

In these LSTMs, at least when i've coded them in pytorch/tf, the LSTM cell keeps the same weights in the chain of LSTM cells to represent a sequence. How does it know when to …

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