Educational platforms have become important tools for e-learning; nonetheless, finding the appropriate educational resources to use often represents a tedious task for learners. Opinions in the educational domain are important information for decision making; they allow teachers to improve the teaching process and enable students to decide on the best educational resources. The large amount of data that is daily generated on the Web makes it difficult, however, to analyze opinions manually. Multiple opinion mining approaches are being proposed as a solution to this problem; this research work introduces EduRP, an education platform that integrates opinion mining techniques and ontology-based user profiling techniques. We specifically propose an opinion mining approach for Spanish text which consists of three main steps: 1) collect opinions from the EduRP platform, 2) process the opinions to normalize the text, and 3) obtain the polarity of the opinions using a machine learning approach. We also propose a profile customization approach that uses Semantic Web technologies, specifically ontologies, to integrate socio-demographic data from different social networks and from the platform itself. Finally, we assess the performance of our system under precision, recall, and F-measure metrics, obtaining average values of 81.85%, 81.80% and 81.54, respectively.

machine learning, natural language processing, opinion mining, sentiment analysis