  Word embeddings or distributed representations of words are being used in
various applications like machine translation, sentiment analysis, topic
identification etc. Quality of word embeddings and performance of their
applications depends on several factors like training method, corpus size and
relevance etc. In this study we compare performance of a dozen of pretrained
word embedding models on lyrics sentiment analysis and movie review polarity
tasks. According to our results, Twitter Tweets is the best on lyrics sentiment
analysis, whereas Google News and Common Crawl are the top performers on movie
polarity analysis. Glove trained models slightly outrun those trained with
Skipgram. Also, factors like topic relevance and size of corpus significantly
impact the quality of the models. When medium or large-sized text sets are
available, obtaining word embeddings from same training dataset is usually the
best choice.
