Inspired by (Lin et al., 2017), we also design a penalization term to ensure the diversity of semantics from different sentiment-resourcespecific sentence representations, which reduces information redundancy from different sentiment resources attention.
the final loss function is presented as follows: where y j i is the target sentiment distribution of the sentence,ŷ j i is the prediction probabilities, θ denotes each parameter to be regularized, Θ is parameter set, λ is the coefficient for L 2 regularization, µ is a hyper-parameter to balance the three terms, ψ is the weight parameter, I denotes the the identity matrix and ||.|| F denotes the Frobenius norm of a matrix.