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Dataset for modeling Beck’s cognitive triad to understand depression
This article presents data to model Beck’s cognitive triad to understand the subjective symptoms of depression, such as negative view of self, future, and world. The Cognitive Triad Dataset (CTD) comprises 5886 messages, 600 from the Time-to-Change blog, 580 from Beyond Blue personal stories, and 47...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487009/ https://www.ncbi.nlm.nih.gov/pubmed/34632022 http://dx.doi.org/10.1016/j.dib.2021.107431 |
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author | Jere, Shreekant Patil, Annapurna P. Shidaganti, Ganeshayya I. Aladakatti, Shweta S. Jayannavar, Laxmi |
author_facet | Jere, Shreekant Patil, Annapurna P. Shidaganti, Ganeshayya I. Aladakatti, Shweta S. Jayannavar, Laxmi |
author_sort | Jere, Shreekant |
collection | PubMed |
description | This article presents data to model Beck’s cognitive triad to understand the subjective symptoms of depression, such as negative view of self, future, and world. The Cognitive Triad Dataset (CTD) comprises 5886 messages, 600 from the Time-to-Change blog, 580 from Beyond Blue personal stories, and 4706 from Twitter. The data were manually labeled by skilled annotators. This data is divided into six categories: self-positive, world-positive, future-positive, self-negative, world-negative, and future-negative. The Cognitive Triad Dataset was evaluated on two subtasks: aspect detection and sentiment classification on given aspects. The dataset will aid in the comprehension of Beck’s Cognitive Triad Inventory (CTI) items in a person’s social media posts. |
format | Online Article Text |
id | pubmed-8487009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-84870092021-10-07 Dataset for modeling Beck’s cognitive triad to understand depression Jere, Shreekant Patil, Annapurna P. Shidaganti, Ganeshayya I. Aladakatti, Shweta S. Jayannavar, Laxmi Data Brief Data Article This article presents data to model Beck’s cognitive triad to understand the subjective symptoms of depression, such as negative view of self, future, and world. The Cognitive Triad Dataset (CTD) comprises 5886 messages, 600 from the Time-to-Change blog, 580 from Beyond Blue personal stories, and 4706 from Twitter. The data were manually labeled by skilled annotators. This data is divided into six categories: self-positive, world-positive, future-positive, self-negative, world-negative, and future-negative. The Cognitive Triad Dataset was evaluated on two subtasks: aspect detection and sentiment classification on given aspects. The dataset will aid in the comprehension of Beck’s Cognitive Triad Inventory (CTI) items in a person’s social media posts. Elsevier 2021-09-25 /pmc/articles/PMC8487009/ /pubmed/34632022 http://dx.doi.org/10.1016/j.dib.2021.107431 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Jere, Shreekant Patil, Annapurna P. Shidaganti, Ganeshayya I. Aladakatti, Shweta S. Jayannavar, Laxmi Dataset for modeling Beck’s cognitive triad to understand depression |
title | Dataset for modeling Beck’s cognitive triad to understand depression |
title_full | Dataset for modeling Beck’s cognitive triad to understand depression |
title_fullStr | Dataset for modeling Beck’s cognitive triad to understand depression |
title_full_unstemmed | Dataset for modeling Beck’s cognitive triad to understand depression |
title_short | Dataset for modeling Beck’s cognitive triad to understand depression |
title_sort | dataset for modeling beck’s cognitive triad to understand depression |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487009/ https://www.ncbi.nlm.nih.gov/pubmed/34632022 http://dx.doi.org/10.1016/j.dib.2021.107431 |
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