METS-CoV
METS-CoV包含10,000条COVID-19相关推文，标注了7种实体类型和定向情感，用于医学命名实体识别和情感分析。
基本信息
资源简介
METS-CoV是由浙江大学公共卫生学院及第二附属医院创建的COVID-19相关推文数据集，包含10,000条推文，标注了7种实体类型（疾病、药物、症状、疫苗、人物、地点、组织），并针对4种实体类型进行了情感标注，用于命名实体识别和定向情感分析任务。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
Tips: 该数据集 Qianfanghub 可以协助提供有偿下载服务，注意，服务不针对数据相关产权，只是技术服务费。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/YLab-Open/METS-CoV.git
curl -L -o repo.zip https://github.com/YLab-Open/METS-CoV/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
This repository includes the dataset and benchmark of the paper:
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets (Submitted to NeurIPS 2022 Track on Datasets and Benchmarks).
Authors: Peilin Zhou, Zeqiang Wang, Dading Chong, Zhijiang Guo, Yining Hua, Zichang Su, Zhiyang Teng, Jiageng Wu, Jie Yang
Abstract
The COVID-19 pandemic continues to bring up various topics discussed or debated on social media.
In order to explore the impact of pandemics on people’‘s lives, it is crucial to understand the public’‘s concerns and attitudes towards pandemic-related entities (e.g., drugs, vaccines) on social media.
However, models trained on existing named entity recognition (NER) or targeted sentiment analysis (TSA) datasets have limited ability to understand COVID-19-related social media texts because these datasets are not designed or annotated from a medical perspective.
In this paper, we release METS-CoV, a dataset containing medical entities and targeted sentiments from COVID-19 related tweets.
METS-CoV contains 10,000 tweets with 7 types of entities, including 4 medical entity types (Disease, Drug, Symptom, and Vaccine) and 3 general entity types (Person, Location, and Organization).
To further investigate tweet users’’ attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9,101 entities (in 5,278 tweets).
To the best of our knowledge, METS-CoV is the first dataset to collect medical entities and corresponding sentiments of COVID-19 related tweets.
We benchmark the performance of classical machine learning models and state-of-the-art deep learning models on NER and TSA tasks with extensive experiments.
Results show that this dataset has vast room for improvement for both NER and TSA tasks.
With rich annotations and comprehensive benchmark results, we believe METS-CoV is a fundamental resource for building better medical social media understanding tools and facilitating computational social science research, especially on epidemiological topics.
Our data, annotation guidelines, benchmark models, and source code are publicly available [link](https://github.com/YLa
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
精度瓶颈？数据缺失？
当前公开数据无法满足您的算法精度？千方提供针对 新型冠状病毒肺炎 的高质量、多模态真实临床数据定制解决方案。
