An aspect-level sentiment analysis dataset for therapies on Twitter

descriptionPublicationkeyboard_double_arrow_right Article 01 Oct 2023 English Publisher:Elsevier BVJournal:Data in Brief, volume 50, page 109,618 (issn: 2352-3409,

Copyright policy )Funded by:NIH | Mining Social Media Big D...

Authors: Yuting Guo; Sudeshna Das; Sahithi Lakamana; Abeed Sarker;

doi: 10.1016/j.dib.2023.109618 , 10.5281/zenodo.8186722 , 10.5281/zenodo.8186910 , 10.5281/zenodo.8186721 , 10.5281/zenodo.10359765

pmid: 37808542

pmc: PMC10558704

An aspect-level sentiment analysis dataset for therapies on Twitter

- Summary
- Subjects
- Metrics

Abstract

This dataset is an aspect-level sentiment analysis dataset for chronic pain therapies, created by leveraging user-generated text from Twitter. The dataset contains a total of 5364 tweets related to 32 chronic pain therapies. These tweets are further categorized into 998 (18.6%) positive, 619 (11.5%) negative, and 3747 (69.9%) neutral sentiments. The inter-annotation agreement for the dataset was evaluated using Cohen's Kappa score, achieving an 0.82 score. At the time of submission, this dataset is used for SMM4H 2023 shared tasks. The labels of the test set will be added after the shared task. More details about the shared task can be found at https://healthlanguageprocessing.org/smm4h-2023/.

Related Organizations

Emory University
United States

Keywords

therapy, Science (General), biomedical informatics, Natural language processing, Computer applications to medicine. Medical informatics, R858-859.7, machine learning;, Sentiment analysis, Q1-390, sentiment analysis, Machine learning, Text classification, Biomedical informatics, Therapy, natural language processing, Data Article

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