Title:
A Content Analysis System That Supports Sentiment Analysis for Subjectivity and Polarity Detection in Online Courses [Descargar]Autores/as:
Cobos, Ruth and Jurado, Francisco and Blázquez-Herranz, Alberto
Términos Índice:
Tools;Education;Sentiment analysis;Feature extraction;Bibliographies;Terminology;MOOC;sentiment analysis;opinion mining;natural language processing;polarity;subjectivity
Resumen:
Given the current and increasing relevance of research aimed towards the optimization of teaching and learning experiences in online Education, a plethora of studies regarding the application of different technologies to this purpose have been developed. Specifically, Natural Language Processing (NLP) has been used to detect potential sentiments and opinions in texts, enabling a broader scope for making inferences. At Universidad Autónoma of Madrid, Spain, we have designed and developed a tool for the application of NLP techniques to analyse the contents of online courses and the contributions of their learners (video transcriptions, readings, questions and answers of the evaluation activities and learner’s posts in forums, among others) to improve the teaching material and the teaching-learning processes of these courses. This tool is called edX-CAS (“Content Analyser System for edX MOOCs”). In this paper, we provide a detailed description of the tool, its functionalities and its NPL processes that support Sentiment Analysis for Subjectivity and Polarity detection. Moreover, we present a review of current research in the field of application of NLP in the improvement of teaching and learning experiences in MOOCs.
DOI:
Cómo citar:
Cobos, Ruth and Jurado, Francisco and Blázquez-Herranz, Alberto, "A Content Analysis System That Supports Sentiment Analysis for Subjectivity and Polarity Detection in Online Courses" in IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, pp. 177-187, Nov. 2019. doi: 10.1109/RITA.2019.2952298