| 000 | 02631naa a2200265 a 4500 | ||
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| 003 | AR-LpUFIB | ||
| 005 | 20250311171158.0 | ||
| 008 | 230201s2022 xx o 000 0 eng d | ||
| 024 | 8 |
_aDIF-M8281 _b8501 _zDIF007573 |
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| 040 |
_aAR-LpUFIB _bspa _cAR-LpUFIB |
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| 100 | 1 | _aTessore, Juan Pablo | |
| 245 | 1 | 0 | _aDistant supervised construction and evaluation of a novel dataset of emotion-tagged social media comments in spanish |
| 500 | _aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca) | ||
| 520 | _aTagged 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 subjectiv- ity, 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. | ||
| 534 | _aCognitive Computation, 2022, 14(2). | ||
| 650 | 4 |
_aREDES SOCIALES _94685 |
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| 650 | 4 | _aTIPOS DE DATOS | |
| 653 | _aminería de texto | ||
| 653 | _adatos de emociones | ||
| 700 | 1 | _aEsnaola, Leonardo Martín | |
| 700 | 1 | _aLanzarini, Laura Cristina | |
| 700 | 1 | _aBaldassarri, Sandra | |
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/s12559-020-09800-x |
| 942 | _cCP | ||
| 999 |
_c57346 _d57346 |
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