THIS PAPER PRESENTS AN APPROACH TO TAKE ADVANTAGE OF REVIEWS WRITTEN IN SPANISH TO GENERATE ASPECT-BASED RECOMMENDATIONS. ALTHOUGH DIVERSE APPROACHES HAVE BEEN PROPOSED AND EVALUATED FOR REVIEWS WRITTEN IN ENGLISH, THERE IS A LACK OF PROPOSALS TO ACCOMPLISH THIS TASK USING REVIEWS IN SPANISH. THE PROPOSED APPROACH USES TEXT MINING TECHNIQUES AND TOOLS TO EXTRACT ITEM ASPECTS AND ESTIMATE USER PREFERENCES FOR EACH OF THEM. THE ESTIMATED PREFERENCES THEN FEED A MULTI-CRITERIA RECOMMENDER SYSTEM, CONSIDERING EACH EXTRACTED ASPECT AS A CRITERION REGARDING USER PREFERENCE. PRELIMINARY RESULTS SHOW THAT, USING ASPECTS EXTRACTED FROM REVIEWS WRITTEN IN SPANISH, IT IS POSSIBLE TO IMPROVE THE QUALITY OF RECOMMENDATIONS GENERATED WITH A TRADITIONAL ALGORITHM. DUE TO THE SIMPLICITY OF THE PROPOSED APPROACH, IT CAN BE EASILY USED BY LOCAL COMPANIES TO INCORPORATE ASPECT-BASED RECOMMENDATIONS.