Normally, a sentiment classifier transforms the final vector v up , usually in a linear fashion, into a vector with a dimension equivalent to the number of classes C. A softmax layer is then used to obtain a probability distribution y over the sentiment classes.
finally, the full model uses a crossentropy over all training documents D as objective function L during training, where y is the gold probability distribution: HCSC has a nice architecture which can be used to improve the training.