SINAI/DOS

---
license: cc-by-nc-sa-4.0
language:
- es
---
# DOS - Drug Opinion Spanish corpus

## Description:
The Drug Opinion Spanish (DOS) corpus has been extracted from the web portal https://www.mimedicamento.es which is an independent platform where users share their experiences with medicines. It consists of 877 comments on the 30 drugs that had received the most reviews on this web portal as of March 14, 2017. Each comment contains information on the date it was posted, the gender and age of the consumer, the ailment treated, the textual opinion and a star rating for the following satisfaction categories: overall, efficacy, number of side effects, severity of side effects and ease of ingestion. In addition, each comment has been annotated at the aspect level with the side effects described in it and with a polarity and intensity label related to the patient's opinion. The corpus has 3,784 sentences containing a total of 2,230 side effects, of which 98 are positive, 2,119 negative and 13 neutral. In relation to the intensity of the side effects, 655 are of high intensity, 1,486 of medium intensity and 89 of low intensity.

## Citation:
```bibtex
@inproceedings{jimenez2017corpus,
title={Corpus annotation for aspect based sentiment analysis in medical domain},
author={Jim{\'e}nez-Zafra, Salud Mar{\i}a and Mart{\i}n-Valdivia, M Teresa},
year={2017},
booktitle={Proceedings of the 2nd International Workshop on Extraction and Processing of Rich Semantics from Medical Texts}
}
```

原始信息汇总

DOS - Drug Opinion Spanish corpus

描述

Drug Opinion Spanish (DOS) 语料库是从网站 https://www.mimedicamento.es 提取的，该网站是一个独立的平台，用户在此分享他们对药物的使用体验。该语料库包含 877 条评论，涉及 2017 年 3 月 14 日之前在该网站上获得最多评论的 30 种药物。每条评论包含以下信息：发布日期、消费者性别和年龄、治疗病症、文本意见以及以下满意度类别的星级评分：总体、疗效、副作用数量、副作用严重程度和服用便利性。此外，每条评论在方面级别上标注了其中描述的副作用，以及与患者意见相关的极性和强度标签。语料库包含 3,784 个句子，共涉及 2,230 个副作用，其中 98 个为正面，2,119 个为负面，13 个为中性。关于副作用的强度，655 个为高强度，1,486 个为中强度，89 个为低强度。

引用

bibtex @inproceedings{jimenez2017corpus, title={Corpus annotation for aspect based sentiment analysis in medical domain}, author={Jim{e}nez-Zafra, Salud Mar{i}a and Mart{i}n-Valdivia, M Teresa}, year={2017}, booktitle={Proceedings of the 2nd International Workshop on Extraction and Processing of Rich Semantics from Medical Texts} }