  This work investigates the role of factors like training method, training
corpus size and thematic relevance of texts in the performance of word
embedding features on sentiment analysis of tweets, song lyrics, movie reviews
and item reviews. We also explore specific training or post-processing methods
that can be used to enhance the performance of word embeddings in certain tasks
or domains. Our empirical observations indicate that models trained with
multithematic texts that are large and rich in vocabulary are the best in
answering syntactic and semantic word analogy questions. We further observe
that influence of thematic relevance is stronger on movie and phone reviews,
but weaker on tweets and lyrics. These two later domains are more sensitive to
corpus size and training method, with Glove outperforming Word2vec. "Injecting"
extra intelligence from lexicons or generating sentiment specific word
embeddings are two prominent alternatives for increasing performance of word
embedding features.
