OVER THE LAST DECADE, THE USE OF SOCIAL MEDIA AS A MASSIVE COMMUNICATION MEDIUM HAS GIVEN PEOPLE A TOOL TO EXPRESS THEIR OPINIONS. IN IT, PEOPLE WRITE THEIR THOUGHTS AND FEELINGS ABOUT PLENTY OF TOPICS GENERATING LARGE AMOUNT OF DATA THAT CAN BE ANALYZED BY COMPANIES AND RESEARCHERS. BEING TASKS OF THE NATURAL LANGUAGE PROCESSING, EMOTION ANALYSIS FOCUSES ON EXTRACTING THE UNDERLYING EMOTIONS IN TEXT, MEANWHILE, SENTIMENT ANALYSIS FOCUSES ON EXTRACTING THE POLARITY OF IT. TO ACCOMPLISH THIS TWO TASKS, TRADITIONAL MACHINE LEARNING AND DEEP LEARNING TECHNIQUES ARE USED. HOWEVER, TO REACH GOOD GENERALIZATION PERFORMANCE, THESE TECHNIQUES REQUIRE LARGE DATASETS OF LABELED DATA FOR TRAINING. FOR RESEARCHERS THIS IS AN ISSUE BECAUSE IN LANGUAGES LIKE SPANISH THE LABELED DATASETS ARE SPARSE. TO SOLVE THIS, DATA AUGMENTATION TECHNIQUES ARE USED TO GENERATE WIDER DATASETS OF LABELED DATA FROM A SMALL, LABELED DATASET. THIS WORK PRESENTS AN OPENMP VERSION FOR SHARED MEMORY SYSTEMS OF A DATA AUGMENTATION TECHNIQUE CALLED POS WISE SYNONYM SUBSTITUTION THAT REPLACES SOME OF THE WORDS OF A SENTENCE WITH THEIR SYNONYMS EXTRACTED FROM WORDNET TO CREATE NEW SENTENCES. WITH THE PARALLEL APPROACH WE REDUCED THE EXECUTION TIME REASONABLY COMPARED TO THE ORIGINAL VERSION REACHING A SPEEDUP OF UP TO 17.5X

OVER 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