# How do I calculate the gradient of the hinge loss function?

With reference to the research paper entitled Sentiment Embeddings with Applications to Sentiment Analysis, I am trying to implement its sentiment ranking model in Python, for which I am required to optimize the following hinge loss function:

$$\operatorname{loss}_{\text {sRank}}=\sum_{t}^{T} \max \left(0,1-\delta_{s}(t) f_{0}^{\text {rank}}(t)+\delta_{s}(t) f_{1}^{\text {rank}}(t)\right)$$

Unlike the usual mean square error, I cannot find its gradient to perform backpropagation.

How do I calculate the gradient of this loss function?

• You will either need to contact the authors and hope at least one responds with the equation or you need to calculate it yourself. The following article might help you: twice22.github.io/hingeloss – Brian O'Donnell Oct 8 '18 at 3:44