# METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets arXiv: https://arxiv.org/abs/2209.13773 ยท HTML: https://mlsanity.com/paper/2209.13773/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 87 references that are in the map. - First posted on arXiv 28 Sep 2022. ## Builds on (most cited first, 27 of 27) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016) - [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015) - [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020) - [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016) - [The Multilingual Amazon Reviews Corpus](https://mlsanity.com/paper/2010.02573/) (2020) - [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020) - [Interactive Attention Networks for Aspect-Level Sentiment Classification](https://mlsanity.com/paper/1709.00893/) (2017) - [Effective LSTMs for Target-Dependent Sentiment Classification](https://mlsanity.com/paper/1512.01100/) (2015) - [Aspect Level Sentiment Classification with Deep Memory Network](https://mlsanity.com/paper/1605.08900/) (2016) - [Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks](https://mlsanity.com/paper/1909.03477/) (2019) - [Attentional Encoder Network for Targeted Sentiment Classification](https://mlsanity.com/paper/1902.09314/) (2019) - [Transformation Networks for Target-Oriented Sentiment Classification](https://mlsanity.com/paper/1805.01086/) (2018) - [LocVTP: Video-Text Pre-training for Temporal Localization](https://mlsanity.com/paper/2207.10362/) (2022) - [Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter](https://mlsanity.com/paper/2105.07148/) (2021) - [A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration](https://mlsanity.com/paper/2004.03688/) (2020) - [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020) - [Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks](https://mlsanity.com/paper/1703.06345/) (2017) - [NCRF++: An Open-source Neural Sequence Labeling Toolkit](https://mlsanity.com/paper/1806.05626/) (2018) - [Design Challenges and Misconceptions in Neural Sequence Labeling](https://mlsanity.com/paper/1806.04470/) (2018) - [YEDDA: A Lightweight Collaborative Text Span Annotation Tool](https://mlsanity.com/paper/1711.03759/) (2017) - [Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision](https://mlsanity.com/paper/2003.12218/) (2020) - [Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis](https://mlsanity.com/paper/2201.07281/) (2022) - [Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications](https://mlsanity.com/paper/2206.14358/) (2022) - [YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews](https://mlsanity.com/paper/2012.14541/) (2020) ## Built on by (most cited first, 12 of 12) - [Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study](https://mlsanity.com/paper/2304.04339/) (2023) - [Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset](https://mlsanity.com/paper/2306.03030/) (2023) - [Exploring Social Media for Early Detection of Depression in COVID-19 Patients](https://mlsanity.com/paper/2302.12044/) (2023) - [ExpNote: Black-box Large Language Models are Better Task Solvers with Experience Notebook](https://mlsanity.com/paper/2311.07032/) (2023) - [A Systematic Review of NeurIPS Dataset Management Practices](https://mlsanity.com/paper/2411.00266/) (2024) - [Scalable Medication Extraction and Discontinuation Identification from Electronic Health Records Using Large Language Models](https://mlsanity.com/paper/2506.11137/) (2025) - [Feature-augmented Machine Reading Comprehension with Auxiliary Tasks](https://mlsanity.com/paper/2211.09438/) (2022) - [A Sentiment Analysis of Medical Text Based on Deep Learning](https://mlsanity.com/paper/2404.10503/) (2024) - [Revealing COVID-19's Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter](https://mlsanity.com/paper/2410.08352/) (2024) - [Opioid Named Entity Recognition (ONER-2025) from Reddit](https://mlsanity.com/paper/2504.00027/) (2025) - [Named Entity Recognition in COVID-19 tweets with Entity Knowledge Augmentation](https://mlsanity.com/paper/2510.04001/) (2025) - [CrisisKD: Five-Stage Knowledge Distillation for Aspect-Level Sentiment and Emotion Analysis in Crisis Discourse](https://mlsanity.com/paper/2609.05757/) (2026) ## Abstract (arXiv) 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. This paper releases 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 the dataset has vast room for improvement for both NER and TSA tasks. METS-CoV is an important resource for developing better medical social media tools and facilitating computational social science research, especially in epidemiology. Our data, annotation guidelines, benchmark models, and source code are publicly available (https://github.com/YLab-Open/METS-CoV) to ensure reproducibility.