It is obvious that computing the distance between x t−i and each opinion word is expensive.
we run an off-the-shelf clustering algorithm over opinion words in the training set and then use the produced n c centroids to estimate the indicator score v I i of w t−i being an opinion word: where x t−i is the distributed representation of w t−i and c j is the centroid vector representation of j-th cluster.