# Word2Vec and Doc2Vec in Unsupervised Sentiment Analysis of Clinical Discharge Summaries

arXiv: https://arxiv.org/abs/1805.00352 · HTML: https://mlsanity.com/paper/1805.00352/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2018.

## Built on by (most cited first, 3 of 3)

- [Contextual Text Embeddings for Twi](https://mlsanity.com/paper/2103.15963/) (2021)
- [T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERT](https://mlsanity.com/paper/2106.01097/) (2021)
- [Generating Cyber Threat Intelligence to Discover Potential Security Threats Using Classification and Topic Modeling](https://mlsanity.com/paper/2108.06862/) (2021)

## Abstract (arXiv)

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.