127639 20241125101127.0 doi 10.1007/s11042-022-13750-x sideral 131936 ART-2023-131936 eng Tessore, Juan Pablo Contextual information usage for the enhancement of basic emotion classification in a weakly labelled social network dataset in Spanish 2023 Access copy available to the general public Unrestricted Basic emotion classification is one of the main tasks of Sentiment Analysis usually performed by using several machine learning techniques. One of the main issues in Sentiment Analysis is the availability of tagged resources to properly train supervised classification algorithms. This is of particular concern in languages other than English, such as Spanish, where scarcity of these resources is the norm. In addition, most basic emotion datasets available in Spanish are rather small, containing a few hundred (or thousand) samples. Usually, the samples only contain a short text (frequently a comment) and a tag (the basic emotion), omitting crucial contextual information that may help to improve the classification task results. In this paper, the impact of using contextual information is measured on a recently published Spanish basic emotion dataset and the baseline architecture proposed in the Semantic Evaluation 2019 competition. This particular dataset has two main advantages for this paper. First, it was compiled using Distant Supervision and as a result it contains several hundred thousand samples. Secondly, the authors included valuable contextual information for each comment. The results show that contextual information, such as news headlines or summaries, helps improve the classification accuracy over a dataset of distantly supervised basic emotion labelled comments. info:eu-repo/grantAgreement/ES/DGA-FEDER/T60-20R-AFFECTIVE LAB info:eu-repo/grantAgreement/ES/MICINN/RTI2018-096986-B-C31 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.0 2023 COMPUTER SCIENCE, INFORMATION SYSTEMS 95 / 250 = 0.38 2023 Q2 T2 ENGINEERING, ELECTRICAL & ELECTRONIC 136 / 353 = 0.385 2023 Q2 T2 COMPUTER SCIENCE, THEORY & METHODS 41 / 144 = 0.285 2023 Q2 T1 COMPUTER SCIENCE, SOFTWARE ENGINEERING 37 / 132 = 0.28 2023 Q2 T1 0.801 2023 Media Technology 2023 Q1 Software 2023 Q2 Computer Networks and Communications 2023 Q2 Hardware and Architecture 2023 Q2 7.2 2023 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Esnaola, Leonardo Martín Ramón, Hugo Dionisio Lanzarini, Laura Baldassarri, Sandra Universidad de Zaragoza (orcid)0000-0002-9315-6391 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 82 (2023), 9871–9890 Multimed. Tools Appl. Multimedia Tools and Applications 1380-7501 1994307 http://zaguan.unizar.es/record/127639/files/texto_completo.pdf Postprint 1147963 http://zaguan.unizar.es/record/127639/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:127639 articulos driver 2024-11-22-11:57:52 ARTICLE