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GUIDE FOR THE APPLICATION OF THE DATA AUGMENTATION APPROACH ON SETS OF TEXTS IN SPANISH FOR SENTIMENT AND EMOTION ANALYSIS

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dc.creatorRODRIGO ANDRÉS GUTIÉRREZ BENÍTEZ
dc.creatorALEJANDRA ANDREA SEGURA NAVARRETE
dc.creatorCHRISTIAN LAUTARO VIDAL CASTRO
dc.date2024
dc.date.accessioned2025-01-10T15:51:07Z
dc.date.available2025-01-10T15:51:07Z
dc.date.issued2024
dc.description.abstractOVER THE LAST TEN YEARS, SOCIAL MEDIA HAS BECOME A CRUCIAL DATA SOURCE FOR BUSINESSES AND RESEARCHERS, PROVIDING A SPACE WHERE PEOPLE CAN EXPRESS THEIR OPINIONS AND EMOTIONS. TO ANALYZE THIS DATA AND CLASSIFY EMOTIONS AND THEIR POLARITY IN TEXTS, NATURAL LANGUAGE PROCESSING (NLP) TECHNIQUES SUCH AS EMOTION ANALYSIS (EA) AND SENTIMENT ANALYSIS (SA) ARE EMPLOYED. HOWEVER, THE EFFECTIVENESS OF THESE TASKS USING MACHINE LEARNING (ML) AND DEEP LEARNING (DL) METHODS DEPENDS ON LARGE LABELED DATASETS, WHICH ARE SCARCE IN LANGUAGES LIKE SPANISH. TO ADDRESS THIS CHALLENGE, RESEARCHERS USE DATA AUGMENTATION (DA) TECHNIQUES TO ARTIFICIALLY EXPAND SMALL DATASETS. THIS STUDY AIMS TO INVESTIGATE WHETHER DA TECHNIQUES CAN IMPROVE CLASSIFICATION RESULTS USING ML AND DL ALGORITHMS FOR SENTIMENT AND EMOTION ANALYSIS OF SPANISH TEXTS. VARIOUS TEXT MANIPULATION TECHNIQUES WERE APPLIED, INCLUDING TRANSFORMATIONS, PARAPHRASING (BACK-TRANSLATION), AND TEXT GENERATION USING GENERATIVE ADVERSARIAL NETWORKS, TO SMALL DATASETS SUCH AS SONG LYRICS, SOCIAL MEDIA COMMENTS, HEADLINES FROM NATIONAL NEWSPAPERS IN CHILE, AND SURVEY RESPONSES FROM HIGHER EDUCATION STUDENTS. THE FINDINGS SHOW THAT THE CONVOLUTIONAL NEURAL NETWORK (CNN) CLASSIFIER ACHIEVED THE MOST SIGNIFICANT IMPROVEMENT, WITH AN 18% INCREASE USING THE GENERATIVE ADVERSARIAL NETWORKS FOR SENTIMENT TEXT (SENTIGAN) ON THE AGGRESSIVENESS (SERIOUSNESS) DATASET. ADDITIONALLY, THE SAME CLASSIFIER MODEL SHOWED AN 11% IMPROVEMENT USING THE EASY DATA AUGMENTATION (EDA) ON THE GENDER-BASED VIOLENCE DATASET. THE PERFORMANCE OF THE BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS (BETO) ALSO IMPROVED BY 10% ON THE BACK-TRANSLATION AUGMENTED VERSION OF THE OCTOBER 18 DATASET, AND BY 4% ON THE EDA AUGMENTED VERSION OF THE TEACHING SURVEY DATASET. THESE RESULTS SUGGEST THAT DATA AUGMENTATION TECHNIQUES ENHANCE PERFORMANCE BY TRANSFORMING TEXT AND ADAPTING IT TO THE SPECIFIC CHARACTERISTICS OF THE DATASET. THROUGH EXPERIMENTATION WITH VARIOUS
dc.formatapplication/pdf
dc.identifier.doi10.1371/journal.pone.0310707
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/13949
dc.languagespa
dc.publisherPLoS One
dc.relation.uri10.1371/journal.pone.0310707
dc.rightsPUBLICADA
dc.subjectSocial media
dc.subjectSentiment analysis
dc.subjectMachine learning
dc.subjectGenerative adversarial networks
dc.subjectEmotion analysis
dc.subjectDeep learning
dc.titleGUIDE FOR THE APPLICATION OF THE DATA AUGMENTATION APPROACH ON SETS OF TEXTS IN SPANISH FOR SENTIMENT AND EMOTION ANALYSIS
dc.typeARTÍCULO
dspace.entity.typePublication
ubb.EstadoPUBLICADA
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.SedeCONCEPCIÓN
ubb.SedeCONCEPCIÓN
ubb.SedeCONCEPCIÓN
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