Since the same words in Figures  3a and 3b have different memory slices due to position weighting and augmented offset feature, as described in Section 3.3, our model predicts opposite sentiments on the two persons.
the model is trained by minimizing the cross entropy plus an L 2 regularization term: where C is the sentiment category set, D is the collection of training data, y ∈ R |C| is a one-hot vector where the element for the true sentiment is 1, f (x; θ) is the predicted sentiment distribution of the model, λ is the weight of L 2 regularization term.