In this paper, we propose a meta-learning approach to learn text emotion distributions from a small sample.
we propose to learn low-rank sentence embeddings by tensor decomposition to capture their contextual semantic similarity, and use K-nearest neighbors (KNNs) of each sentence in the embedding space to generate sample clusters.