accuracy of sentiment classification of tweets for various classifiers 1. The original Google News word2vec embeddings. 2. word2vec augmented with emoji embeddings trained by Barbieri et al. (2016). (using skip-gram neural embedding model by (Mikolov et al., 2013)) 3. word2vec augmented with emoji2vec trained from Unicode descriptions. • Datase: • 67k English tweets labelled manually for positive, neutral, or negative sentiment by Kralj Novak et al. (2015) • In both the training set and the test set, 46% of tweets are labeled neutral, 29% are labeled positive, and 25% are labeled negative.