Sergio Luján Mora

Profesor Titular de Universidad

Application of text mining on social network messages about a MOOC

Diego Buenaño-Fernández, Sergio Luján-Mora, William Villegas-Ch
Proceedings of the 10th International Conference of Education, Research and Innovation (ICERI 2017), p. 6336-6344, Seville (Spain), November 16-18 2017. ISBN: 978-84-697-6957-7. https://doi.org/10.21125/iceri.2017.1641
(ICERI'17c) Congreso internacional / International conference

Resumen

Globalization and the proliferation of Massive Open Online Courses (MOOC) has radically altered the model of education. New technology in this field offers the opportunity to increase the availability of courses to a far greater audience than that provided in the traditional setting. However, the implementation has significant challenges that must be overcome to allow students to take full advantage of them. The flexibility of the platforms in which MOOC operate, and the large amount of learning resources they provide, allows for the inclusion of large numbers of students. This interaction between students and systems produces massive learning behaviour data and leaves traces of the educational process on systems that are useful for analysis. Educational data mining has become an important discipline for discovering new and potentially useful information on large amounts of data. In this article, we study the analysis of sentiment of an MOOC, through the application of text mining techniques on messages received in the social network Twitter. Given the large volume of tweets that are generated around a MOOC, it is convenient to develop methods that are oriented to the processing of texts automatically with an acceptable accuracy. The purpose of this study is to analyse students' opinions about their courses, their instructors, and the main tools used on the course. The analysis focuses on the calculation and analysis of the frequency of terms, the analysis of concordances, groupings and n-grams. In addition, a model was made for classification of tweets according to their polarity of sentiment, based on the algorithm Support Vector Machine.

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