It has been shown that using word embeddings as features helps to improve general performance on many NLP tasks (Turian et al., 2010).
(2011), which reached 88.90% accuracy with a mix of unsupervised and supervised techniques to learn word vectors capturing semantic termdocument information, as well as rich sentiment content.