000127639 001__ 127639
000127639 005__ 20241125101127.0
000127639 0247_ $$2doi$$a10.1007/s11042-022-13750-x
000127639 0248_ $$2sideral$$a131936
000127639 037__ $$aART-2023-131936
000127639 041__ $$aeng
000127639 100__ $$aTessore, Juan Pablo
000127639 245__ $$aContextual information usage for the enhancement of basic emotion classification in a weakly labelled social network dataset in Spanish
000127639 260__ $$c2023
000127639 5060_ $$aAccess copy available to the general public$$fUnrestricted
000127639 5203_ $$aBasic 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.
000127639 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T60-20R-AFFECTIVE LAB$$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-096986-B-C31
000127639 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000127639 590__ $$a3.0$$b2023
000127639 592__ $$a0.801$$b2023
000127639 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b95 / 250 = 0.38$$c2023$$dQ2$$eT2
000127639 593__ $$aMedia Technology$$c2023$$dQ1
000127639 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b136 / 353 = 0.385$$c2023$$dQ2$$eT2
000127639 593__ $$aSoftware$$c2023$$dQ2
000127639 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b41 / 144 = 0.285$$c2023$$dQ2$$eT1
000127639 593__ $$aComputer Networks and Communications$$c2023$$dQ2
000127639 591__ $$aCOMPUTER SCIENCE, SOFTWARE ENGINEERING$$b37 / 132 = 0.28$$c2023$$dQ2$$eT1
000127639 593__ $$aHardware and Architecture$$c2023$$dQ2
000127639 594__ $$a7.2$$b2023
000127639 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000127639 700__ $$aEsnaola, Leonardo Martín
000127639 700__ $$aRamón, Hugo Dionisio
000127639 700__ $$aLanzarini, Laura
000127639 700__ $$0(orcid)0000-0002-9315-6391$$aBaldassarri, Sandra$$uUniversidad de Zaragoza
000127639 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000127639 773__ $$g82 (2023), 9871–9890$$pMultimed. Tools Appl.$$tMultimedia Tools and Applications$$x1380-7501
000127639 8564_ $$s1994307$$uhttps://zaguan.unizar.es/record/127639/files/texto_completo.pdf$$yPostprint
000127639 8564_ $$s1147963$$uhttps://zaguan.unizar.es/record/127639/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000127639 909CO $$ooai:zaguan.unizar.es:127639$$particulos$$pdriver
000127639 951__ $$a2024-11-22-11:57:52
000127639 980__ $$aARTICLE