  In this study, we explored application of Word2Vec and Doc2Vec for sentiment
analysis of clinical discharge summaries. We applied unsupervised learning
since the data sets did not have sentiment annotations. Note that unsupervised
learning is a more realistic scenario than supervised learning which requires
an access to a training set of sentiment-annotated data. We aim to detect if
there exists any underlying bias towards or against a certain disease. We used
SentiWordNet to establish a gold sentiment standard for the data sets and
evaluate performance of Word2Vec and Doc2Vec methods. We have shown that the
Word2vec and Doc2Vec methods complement each other results in sentiment
analysis of the data sets.
