Instead of building a new word embedding model from labeled data, we propose a word vector refinement model to refine existing semantically oriented word vectors using sentiment lexicons.
the proposed model can be applied to the pre-trained vectors obtained by any word representation learning models (e.g., Word2vec and GloVe) as a post-processing step to adapt the pre-trained vectors to sentiment applications.