Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tessore, J.P. ; Esnaola, L.M. ; Lanzarini, L. ; Baldassarri, S. (Universidad de Zaragoza)

Resumen: Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field.

Idioma: Inglés

DOI: 10.1007/s12559-020-09800-x

Año: 2022

Publicado en: COGNITIVE COMPUTATION 14 (2022), 407–424
ISSN: 1866-9956

Factor impacto JCR: 5.4 (2022)

Categ. JCR: NEUROSCIENCES rank: 61 / 272 = 0.224 (2022) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 47 / 145 = 0.324 (2022) - Q2 - T1

Factor impacto CITESCORE: 7.7 - Neuroscience (Q1) - Computer Science (Q1)

Factor impacto SCIMAGO: 1.037 - Computer Science Applications (Q1) - Computer Vision and Pattern Recognition (Q1) - Cognitive Neuroscience (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T60-20R-AFFECTIVE LAB
Financiación: info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/RTI2018-096986-B-C31

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)