Contrary to the approach by<cite> (Tang et al., 2014b)</cite> , we didn't integrate the sentiment information in the word embeddings training process, but rather the sentiment-specific nature of the embeddings was reflected in the choice of different training datasets, yielding different word embedding features for positive and negative tweets.