The above previous work focused on learning sentiment of unigrams, and optionally of multi-word expressions (conflated into single tokens), and used word2vec embeddings (Mikolov et al., 2013).
we are interested in learning sentiment composition from bigrams, and therefore we are mostly interested in compositional bigrams, and aim to construct a sentiment lexicon that contains hundreds of thousands of them.