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In this study, we explored application of Word2Vec and Doc2Vec for sentimentanalysis of clinical discharge summaries. We applied unsupervised learningsince the data sets did not have sentiment annotations. Note that unsupervisedlearning is a more realistic scenario than supervised learning which requiresan access to a training set of sentiment-annotated data. We aim to detect ifthere exists any underlying bias towards or against a certain disease. We usedSentiWordNet to establish a gold sentiment standard for the data sets andevaluate performance of Word2Vec and Doc2Vec methods. We have shown that theWord2vec and Doc2Vec methods complement each other results in sentimentanalysis of the data sets.

Word2Vec and Doc2Vec in Unsupervised Sentiment Analysis of Clinical Discharge Summaries