Dataset of userʼs emotional reactions to news articles

This paper presents a dataset of news articles collected from several Mexican news sources and users' emotional reactions to these news articles. Users were presented with the collected news and, after reading it, were asked to indicate the emotion it provoked in them. The emotions considered were happy, surprised, inspired, moved, neutral, sad, fearful, and angry. This dataset offers a particular focus on emotional reactions from the reader's perspective and has great potential for use in sentiment analysis, linguistics, and media studies research. Furthermore, the news is in Spanish which is a language where resources of this type are scarce. The detailed data collection methodology provides a solid foundation for future research and development in emotion-aware news recommendation systems.

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Publication Details

Journal
Data in Brief
Published
2024-11-19
DOI
https://doi.org/10.1016/j.dib.2024.111158
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
0.00

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article

Dataset of userʼs emotional reactions to news articles

Jessica-Alejandra Castillo-Montoya, Omar Juárez Gambino, Marco-Antonio Torres-López, Consuelo-Varinia García-Mendoza et al.
Data in Brief
Sentiment Analysis and Opinion Mining
article

Dataset of userʼs emotional reactions to news articles

Jessica-Alejandra Castillo-Montoya, Omar Juárez Gambino, Marco-Antonio Torres-López, Consuelo-Varinia García-Mendoza, Tania Rosales-Onofre, Juan Bautista, Yael-Alexandr Sanchez-Mederos
article en

Abstract

This paper presents a dataset of news articles collected from several Mexican news sources and users' emotional reactions to these news articles. Users were presented with the collected news and, after reading it, were asked to indicate the emotion it provoked in them. The emotions considered were happy, surprised, inspired, moved, neutral, sad, fearful, and angry. This dataset offers a particular focus on emotional reactions from the reader's perspective and has great potential for use in sentiment analysis, linguistics, and media studies research. Furthermore, the news is in Spanish which is a language where resources of this type are scarce. The detailed data collection methodology provides a solid foundation for future research and development in emotion-aware news recommendation systems.

Data in BriefVol. 57
Instituto Politécnico Nacional (MX)
Instituto Politécnico Nacional, Comisión de Operación y Fomento de Actividades Académicas, Instituto Politécnico Nacional
Openalex Percentile: Top 81%
Sentiment Analysis and Opinion Mining
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