Artículos científicos

PURPOSE:THIS PAPER AIMS TO DESCRIBE THE PROCESS USED TO CREATE AN EMOTION LEXICON ENRICHED WITH THE EMOTIONAL INTENSITY OF WORDS AND FOCUSES ON IMPROVING THE EMOTION ANALYSIS PROCESS IN TEXTS. DESIGN/METHODOLOGY/APPROACH ? THE PROCESS INCLUDES SETTING, PREPARATION AND LABELLING STAGES. IN THE FIRST STAGE, A LEXICON IS SELECTED. IT MUST INCLUDE A TRANSLATION TO THE TARGET LANGUAGE AND LABELLING ACCORDING TO PLUTCHIK?S EIGHT EMOTIONS. THE SECOND STAGE STARTS WITH THE VALIDATION OF THE TRANSLATIONS. THEN, IT IS EXPANDED WITH THE SYNONYMS OF THE EMOTION SYNSETS OF EACH WORD. IN THE LABELLING STAGE, THE SIMILARITY OF WORDS IS CALCULATED AND DISPLAYED USING WORDNET SIMILARITY. FINDINGS ? THE AUTHORS? APPROACH SHOWS BETTER PERFORMANCE TO IDENTIFICATION OF THE PREDOMINANT EMOTION FOR THE SELECTED CORPUS. THE MOST RELEVANT IS THE IMPROVEMENT OBTAINED IN THE RESULTS OF THE EMOTION ANALYSIS IN A HYBRID APPROACH COMPARED TO THE RESULTS OBTAINED IN A PURIST APPROACH. RESEARCH LIMITATIONS/IMPLICATIONS ? THE PROPOSED LEXICON CAN STILL BE ENRICHED BY INCORPORATING ELEMENTS SUCH AS EMOJIS, IDIOMS AND COLLOQUIAL EXPRESSIONS. PRACTICAL IMPLICATIONS ? THIS WORK IS PART OF A RESEARCH PROJECT THAT AIDS IN SOLVING PROBLEMS IN A DIGITAL SOCIETY, SUCH AS DETECTING CYBERBULLYING, ABUSIVE LANGUAGE AND GENDER VIOLENCE IN TEXTS OR EXERCISING PARENTAL CONTROL. DETECTION OF DEPRESSIVE STATES IN YOUNG PEOPLE AND CHILDREN IS ADDED. ORIGINALITY/VALUE ? THIS SEMI-AUTOMATIC PROCESS CAN BE APPLIED TO ANY LANGUAGE TO GENERATE AN EMOTION LEXICON. THIS RESOURCE WILL BE AVAILABLE IN A SOFTWARE TOOL THAT IMPLEMENTS A CROWDSOURCING STRATEGY ALLOWING THE INTENSITY TO BE RE-LABELLED AND NEW WORDS TO BE AUTOMATICALLY INCORPORATED INTO THE LEXICON.

THE INFORMATION RETRIEVAL PROCESS USE KNOWLEDGE MODELS FOR QUERY EXPANSION, RECOMMENDATION, INDEXING AND/OR DIGITAL RESOURCE LABELLING. EVEN THOUGH IT IS GENERALLY ASSUMED THAT THE MODEL CONTAINS THE CONCEPT BEING SOUGHT, OCCASIONALLY THE CONCEPT IS ABSENT FROM THE MODEL (MATCHING PROBLEM). IN THIS CASE, THOSE CONCEPTS IN THE KNOWLEDGE MODEL THAT ARE SEMANTICALLY OR SYNTACTICALLY CLOSEST TO THE QUERY MAY BE RETRIEVED INSTEAD, THUS ALLOWING ACCESS TO THE KNOWLEDGE REPRESENTED IN THE MODEL, SO IT CAN BE USED IN AN INFORMATION RETRIEVAL PROCESS. THIS PAPER PROPOSES AN ALGORITHM, CALLED THE BEST CANDIDATE ALGORITHM (BC ALGORITHM), TO FIND THE CLOSEST CONCEPT TO A QUERY IN THE MATCHING PROBLEM CONTEXT. IN THIS PAPER, THERE WERE USED FORMAL ONTOLOGIES AS MODELS OF KNOWLEDGE. WHEN A QUERY IS ABSENT FROM THE MODEL, THE ALGORITHM PROPOSES A LIST OF CANDIDATES THAT ARE SORTED BASED ON SYNTACTIC AND SEMANTIC INDEXES PREVIOUSLY DEFINED. THE PROPOSAL WAS EVALUATED THROUGH TWO EXPERIMENTS THAT LED TO THE CONCLUSION THAT IT IS POSSIBLE TO FIND A CLOSEST CONCEPT TO A QUERY IN DIFFERENT DOMAINS OF KNOWLEDGE AND WHEN THERE IS LITTLE INFORMATION ABOUT THE QUERY CONTEXT, SPECIFICALLY, WE ONLY KNOW THE QUERY AND THE DOMAIN OF KNOWLEDGE WHERE IT IS IMMERSED.

THIS PAPER PROPOSES AN EXPANSION OF QUERIES BASED ON FORMAL DOMAIN ONTOLOGIES IN THE CONTEXT OF THE SEARCH FOR LEARNING RESOURCES IN REPOSITORIES. THE EXPANSION PROCESS USES THE RELATION TYPES THAT ARE REPRESENTED IN THESE MODELS; COMMON ONTOLOGICAL RELATIONS, AND ONTOLOGICAL RELATIONS SPECIFIC TO DOMAIN AND TRADITIONAL TERMINOLOGY RELATIONS, TYPICAL OF THESAURI. THE TESTS WERE CONDUCTED USING GENE ONTOLOGY AS THE KNOWLEDGE BASE AND MERLOT IS USED AS THE TEST REPOSITORY. THE RESULTS OF THIS STUDY CASE INDICATE THAT, AT SIMILAR LEVELS OF PRECISION, EXPANDED QUERIES IMPROVE LEVELS OF NOVELTY AND COVERAGE COMPARED TO THE ORIGINAL QUERY (WITHOUT EXPANSION), I.E. EXPANDED QUERIES ALLOW THE USER TO RETRIEVE RELEVANT OBJECTS, WHICH MIGHT NOT BE OBTAINED WITHOUT EXPANSION.

IN RECENT YEARS, THE USE OF SOCIAL NETWORKS HAS INCREASED EXPONENTIALLY, WHICH HAS LED TO A SIGNIFICANT INCREASE IN CYBERBULLYING. CURRENTLY, IN THE FIELD OF COMPUTER SCIENCE, RESEARCH HAS BEEN MADE ON HOW TO DETECT AGGRESSIVENESS IN TEXTS, WHICH IS A PRELUDE TO DETECTING CYBERBULLYING. IN THIS FIELD, THE MAIN WORK HAS BEEN DONE FOR ENGLISH LANGUAGE TEXTS, MAINLY USING MACHINE LEARNING (ML) APPROACHES, LEXICON APPROACHES TO A LESSER EXTENT, AND VERY FEW WORKS USING HYBRID APPROACHES. IN THESE, LEXICONS AND MACHINE LEARNING ALGORITHMS ARE USED, SUCH AS COUNTING THE NUMBER OF BAD WORDS IN A SENTENCE USING A LEXICON OF BAD WORDS, WHICH SERVES AS AN INPUT FEATURE FOR CLASSIFICATION ALGORITHMS. THIS RESEARCH AIMS AT CONTRIBUTING TOWARDS DETECTING AGGRESSIVENESS IN SPANISH LANGUAGE TEXTS BY CREATING DIFFERENT MODELS THAT COMBINE THE LEXICONS AND ML APPROACH. TWENTY-TWO MODELS THAT COMBINE TECHNIQUES AND ALGORITHMS FROM BOTH APPROACHES ARE PROPOSED, AND FOR THEIR APPLICATION, CERTAIN HYPERPARAMETERS ARE ADJUSTED IN THE TRAINING DATASETS OF THE CORPORA, TO OBTAIN THE BEST RESULTS IN THE TEST DATASETS. THREE SPANISH LANGUAGE CORPORA ARE USED IN THE EVALUATION: CHILEAN, MEXICAN, AND CHILEAN-MEXICAN CORPORA. THE RESULTS INDICATE THAT HYBRID MODELS OBTAIN THE BEST RESULTS IN THE 3 CORPORA, OVER IMPLEMENTED MODELS THAT DO NOT USE LEXICONS. THIS SHOWS THAT BY MIXING APPROACHES, AGGRESSIVENESS DETECTION IMPROVES. FINALLY, A WEB APPLICATION IS DEVELOPED THAT GIVES APPLICABILITY TO EACH MODEL BY CLASSIFYING TWEETS, ALLOWING EVALUATING THE PERFORMANCE OF MODELS WITH EXTERNAL CORPUS AND RECEIVING FEEDBACK ON THE PREDICTION OF EACH ONE FOR FUTURE RESEARCH. IN ADDITION, AN API IS AVAILABLE THAT CAN BE INTEGRATED INTO TECHNOLOGICAL TOOLS FOR PARENTAL CONTROL, ONLINE PLUGINS FOR WRITING ANALYSIS IN SOCIAL NETWORKS, AND EDUCATIONAL TOOLS, AMONG OTHERS.

HOPFIELD NEURAL NETWORKS (HNNS) ARE RECURRENT NEURAL NETWORKS USED TO IMPLEMENT ASSOCIATIVE MEMORY. THEY CAN BE APPLIED TO PATTERN RECOGNITION, OPTIMIZATION, OR IMAGE SEGMENTATION. HOWEVER, SOMETIMES IT IS NOT EASY TO PROVIDE THE USERS WITH GOOD EXPLANATIONS ABOUT THE RESULTS OBTAINED WITH THEM DUE TO MAINLY THE LARGE NUMBER OF CHANGES IN THE STATE OF NEURONS (AND THEIR WEIGHTS) PRODUCED DURING A PROBLEM OF MACHINE LEARNING. THERE ARE CURRENTLY LIMITED TECHNIQUES TO VISUALIZE, VERBALIZE, OR ABSTRACT HNNS. THIS PAPER OUTLINES HOW WE CAN CONSTRUCT AUTOMATIC VIDEO-GENERATION SYSTEMS TO EXPLAIN ITS EXECUTION. THIS WORK CONSTITUTES A NOVEL APPROACH TO OBTAIN EXPLAINABLE ARTIFICIAL INTELLIGENCE SYSTEMS IN GENERAL AND HNNS IN PARTICULAR BUILDING ON THE THEORY OF DATA-TO-TEXT SYSTEMS AND SOFTWARE VISUALIZATION APPROACHES. WE PRESENT A COMPLETE METHODOLOGY TO BUILD THESE KINDS OF SYSTEMS. SOFTWARE ARCHITECTURE IS ALSO DESIGNED, IMPLEMENTED, AND TESTED. TECHNICAL DETAILS ABOUT THE IMPLEMENTATION ARE ALSO DETAILED AND EXPLAINED. WE APPLY OUR APPROACH TO CREATING A COMPLETE EXPLAINER VIDEO ABOUT THE EXECUTION OF HNNS ON A SMALL RECOGNITION PROBLEM. FINALLY, SEVERAL ASPECTS OF THE VIDEOS GENERATED ARE EVALUATED (QUALITY, CONTENT, MOTIVATION AND DESIGN/PRESENTATION).

EXECUTION TRACES COMPREHENSION IS AN IMPORTANT TOPIC IN COMPUTER SCIENCE SINCE IT ALLOWS SOFTWARE ENGINEERS TO GET A BETTER UNDERSTANDING OF THE SYSTEM BEHAVIOR. HOWEVER, TRACES ARE USUALLY VERY LARGE AND HENCE THEY ARE DIFFICULT TO INTERPRET. PARALLEL, EXECUTION TRACES COMPREHENSION IS A VERY IMPORTANT TOPIC INTO THE ALGORITHMS LEARNING COURSES SINCE IT ALLOWS STUDENTS TO GET A BETTER UNDERSTANDING OF THE ALGORITHM BEHAVIOR. THEREFORE, THERE IS A NEED TO INVESTIGATE WAYS TO HELP STUDENTS (AND TEACHERS) FIND AND UNDERSTAND IMPORTANT INFORMATION CONVEYED IN A TRACE DESPITE THE TRACE BEING MASSIVE. IN THIS PAPER, WE PROPOSE A NEW APPROXIMATION FOR EXECUTION TRACES COMPREHENSION BASED ON FUZZY LINGUISTIC DESCRIPTIONS. A NEW METHODOLOGY AND A DATA-DRIVEN ARCHITECTURE BASED ON LINGUISTIC MODELLING OF COMPLEX PHENOMENON ARE PRESENTED AND EXPLAINED. IN PARTICULAR, THEY ARE APPLIED TO AUTOMATICALLY GENERATE LINGUISTIC REPORTS FROM EXECUTION TRACES GENERATED DURING THE EXECUTION OF ALGORITHM IMPLEMENTED BY THE STUDENTS OF AN INTRODUCTORY COURSE OF ARTIFICIAL INTELLIGENCE. TO THE BEST OF OUR KNOWLEDGE, IT IS THE FIRST TIME THAT LINGUISTIC MODELLING OF COMPLEX PHENOMENON IS APPLIED TO EXECUTION TRACES COMPREHENSION. THROUGHOUT THE ARTICLE, IT IS SHOWN HOW THIS KIND OF TECHNOLOGY CAN BE EMPLOYED AS A USEFUL COMPUTER-ASSISTED ASSESSMENT TOOL THAT PROVIDES STUDENTS AND TEACHERS WITH TECHNICAL, IMMEDIATE AND PERSONALISED FEEDBACK ABOUT THE ALGORITHMS THAT ARE BEING STUDIED AND IMPLEMENTED. AT THE SAME TIME, THEY PROVIDE US WITH TWO USEFUL APPLICATIONS: THEY ARE AN INDISPENSABLE PEDAGOGICAL RESOURCE FOR IMPROVING COMPREHENSION OF EXECUTION TRACES, AND THEY PLAY AN IMPORTANT ROLE IN THE PROCESS OF MEASURING AND EVALUATING THE ?BELIEVABILITY? OF THE AGENTS IMPLEMENTED. TO SHOW AND EXPLORE THE POSSIBILITIES OF THIS NEW TECHNOLOGY, A WEB PLATFORM HAS BEEN DESIGNED AND IMPLEMENTED BY ONE OF THE AUTHORS, AND IT HAS BEEN INCORPORATED INTO THE PROCESS OF ASSESSMENT OF AN INTRODUCTORY ARTIF

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

THIS PAPER AIMS TO PROPOSE A NEW VERSION OF THE TURING TEST FOR COMPUTER GAME BOTS BASED ON LINGUISTIC DESCRIPTION OF COMPLEX PHENOMENA AND RESTRICTED EQUIVALENCE FUNCTIONS WHOSE GOAL IS TO EVALUATE THE """"BELIEVABILITY"""" OF THE COMPUTER GAMES BOTS ACTING IN A VIRTUAL WORLD. A DATA-DRIVEN SOFTWARE ARCHITECTURE BASED ON LINGUISTIC MODELLING OF COMPLEX PHENOMENA IS ALSO PROPOSED WHICH ALLOWS US TO AUTOMATICALLY GENERATE BOTS BEHAVIOR PROFILES WHICH CAN BE COMPARED WITH HUMAN PLAYERS BEHAVIOR PROFILES IN ORDER TO PROVIDE US WITH A SIMILARITY MEASURE OF BELIEVABILITY BETWEEN THEM.

THIS PAPER AIMS TO PROPOSE A METHOD FOR AUTOMATICALLY LABELLING AN AFFECTIVE LEXICON WITH INTENSITY VALUES BY USING THE WORDNET SIMILARITY (WS) SOFTWARE PACKAGE WITH THE PURPOSE OF IMPROVING THE RESULTS OF AN AFFECTIVE ANALYSIS PROCESS, WHICH IS RELEVANT TO INTERPRETING THE TEXTUAL INFORMATION THAT IS AVAILABLE IN SOCIAL NETWORKS. THE HYPOTHESIS STATES THAT IT IS POSSIBLE TO IMPROVE AFFECTIVE ANALYSIS BY USING A LEXICON THAT IS ENRICHED WITH THE INTENSITY VALUES OBTAINED FROM SIMILARITY METRICS. ENCOURAGING RESULTS WERE OBTAINED WHEN AN AFFECTIVE ANALYSIS BASED ON A LABELLED LEXICON WAS COMPARED WITH THAT BASED ON ANOTHER LEXICON WITHOUT INTENSITY VALUES. DESIGN/METHODOLOGY/APPROACH THE AUTHORS PROPOSE A METHOD FOR THE AUTOMATIC EXTRACTION OF THE AFFECTIVE INTENSITY VALUES OF WORDS USING THE SIMILARITY METRICS IMPLEMENTED IN WS. FIRST, THE INTENSITY VALUES WERE CALCULATED FOR WORDS HAVING AN AFFECTIVE ROOT IN WORDNET. THEN, TO EVALUATE THE EFFECTIVENESS OF THE PROPOSAL, THE RESULTS OF THE AFFECTIVE ANALYSIS BASED ON A LABELLED LEXICON WERE COMPARED TO THE RESULTS OF AN ANALYSIS WITH AND WITHOUT AFFECTIVE INTENSITY VALUES.

OBJECTIVES: THIS PAPER EXPLORES THE POPULAR BELIEF THAT ALL NEWS IS BAD NEWS. MANY CLAIM NOT TO READ NEWSPAPERS TO AVOID KNOWING ABOUT THE WORST OF OUR SOCIETY. WE WANT TEAR DOWN THE MYTH BY APPLYING A SENTIMENT ANALYSIS (SA) APPROACH. METHOD/ANALYSIS: THIS WORK APPLIES SENTIMENT ANALYSIS TECHNIQUES TO STUDY THE HEADLINE BIAS OF ONLINE NEWSPAPERS FOR THE PERIOD BETWEEN MARCH 2014 AND APRIL 2015. WE ANALYZED 2953 HEADLINES GATHERED FROM FIVE OF THE MOST POPULAR CHILEAN NEWSPAPERS WHICH ARE AVAILABLE ONLINE AND OFFER RSS FEEDS. FINDINGS: OUR RESULTS SHOW A ROUGHLY EQUIVALENT PERCENTAGE OF POSITIVE BIAS (38%) AND NEGATIVE BIAS (37%) INSTANCES, WITH 25% OF HEADLINES EXHIBITING A NEUTRAL BIAS. AUTOMATIC CLASSIFI CATION PERFORMANCE IS PROMISING, WITH DECENT CLASSIFIER PERFORMANCE AND SENSITIVITY, WITH PLENTY OF ROOM FOR IMPROVEMENT. NOVELTY/IMPROVEMENT: THIS WORK ALSO A DOMAIN-SPECIFIC SPANISH LANGUAGE TAGGED CORPUS WAS GENERATED AS A RESULT OF THIS WORK, WHICH IS A VALUABLE RESOURCE FOR FUTURE STUDIES.

SENTIMENT ANALYSIS (SA) IS A USEFUL AND IMPORTANT DISCIPLINE IN COMPUTER SCIENCE, AS IT ALLOWS HAVING A KNOWLEDGE BASE ABOUT THE OPINIONS OF PEOPLE REGARDING A TOPIC. THIS KNOWLEDGE IS USED TO IMPROVE DECISION-MAKING PROCESSES. ONE APPROACH TO ACHIEVE THIS IS BASED ON THE USE OF LEXICAL KNOWLEDGE STRUCTURES. IN PARTICULAR, OUR AIM IS TO ENRICH AN AFFECTIVE LEXICON BY THE ANALYSIS OF THE SIMILARITY RELATIONSHIP BETWEEN WORDS. THE HYPOTHESIS OF THIS WORK STATES THAT THE SIMILARITIES OF THE WORDS BELONGING TO AN AFFECTIVE CATEGORY, WITH RESPECT TO ANY OTHER WORD, BEHAVE IN A HOMOGENEOUS WAY WITHIN EACH AFFECTIVE CATEGORY. THE EXPERIMENTAL RESULTS SHOW THAT WORDS OF A SAME AFFECTIVE CATEGORY HAVE A HOMOGENEOUS SIMILARITY WITH AN ANTONYM, AND THAT THE SIMILARITIES OF THESE WORDS WITH ANY OF THEIR ANTONYMS HAVE A LOW VARIABILITY. THE NOVELTY OF THIS PAPER IS THAT IT BUILDS THE BASES OF A MECHANISM THAT ALLOWS INCORPORATING THE INTENSITY IN AN AFFECTIVE LEXICON AUTOMATICALLY.

PURPOSE THIS PAPER AIMS TO SHOW THE RESULTS OBTAINED FROM THE DATA MINING TECHNIQUES APPLICATION TO LEARNING OBJECTS (LO) METADATA. DESIGN/METHODOLOGY/APPROACH A GENERAL REVIEW OF THE LITERATURE WAS CARRIED OUT. THE AUTHORS GATHERED AND PRE?PROCESSED THE DATA, AND THEN ANALYZED THE RESULTS OF DATA MINING TECHNIQUES APPLIED UPON THE LO METADATA. FINDINGS IT IS POSSIBLE TO EXTRACT NEW KNOWLEDGE BASED ON LEARNING OBJECTS STORED IN REPOSITORIES. FOR EXAMPLE IT IS POSSIBLE TO IDENTIFY DISTINCTIVE FEATURES AND GROUP LEARNING OBJECTS ACCORDING TO THEM. SEMANTIC RELATIONSHIPS CAN ALSO BE FOUND AMONG THE ATTRIBUTES THAT DESCRIBE LEARNING OBJECTS. RESEARCH LIMITATIONS/IMPLICATIONS IN THE FIRST SECTION, FOUR TEST REPOSITORIES ARE INCLUDED FOR CASE STUDY. IN THE SECOND SECTION, THE ANALYSIS IS FOCUSED ON THE MOST COMPLETE REPOSITORY FROM THE PEDAGOGICAL POINT OF VIEW. ORIGINALITY/VALUE MANY PUBLICATIONS REPORT RESULTS OF ANALYSIS ON REPOSITORIES MAINLY FOCUSED ON THE NUMBER, EVOLUTION AND GROWTH OF THE LEARNING OBJECTS. BUT, THERE IS A SHORTAGE OF RESEARCH USING DATA MINING TECHNIQUES ORIENTED TO EXTRACT NEW SEMANTIC KNOWLEDGE BASED ON LEARNING OBJECTS METADATA.