2 research outputs found
Sharing Linkable Learning Objects with the use of Metadata and a Taxonomy Assistant for Categorization
In this work, a re-design of the Moodledata module functionalities is
presented to share learning objects between e-learning content platforms, e.g.,
Moodle and G-Lorep, in a linkable object format. The e-learning courses content
of the Drupal-based Content Management System G-Lorep for academic learning is
exchanged designing an object incorporating metadata to support the reuse and
the classification in its context. In such an Artificial Intelligence
environment, the exchange of Linkable Learning Objects can be used for dialogue
between Learning Systems to obtain information, especially with the use of
semantic or structural similarity measures to enhance the existent Taxonomy
Assistant for advanced automated classification
Emotional book classification from book blurbs
Knowing and predicting opinions of people is considered a strategic added value, interpreting the qualia i.e., the subjective nature of emotional content. The aim of this work is to study the feasibility of an emotion recognition and automated classification of books according to emotional tags, by means of a lexical and semantic analysis of book blurbs. A supervised learning approach is used to determine if a correlation exists between the characteristics of a book blurb and emotional icons associated to the book by users. In this paper the underlying idea of the system is presented, the preprocessing and features extraction phases are described and experimental results on the social network Zazie and its mood tags are discussed