How to extract meaningful Insights from UGC: A Knowledge-Based Method applied to Education

New analysis and visualization techniques are required to cull useful insights from the vast amounts of data generated by new technologies and data sharing platforms. The aim of this article is to lay a foundation for such techniques so that the age of big data may also be the age of knowledge, visualization and understanding. Education is the keystone industry used in this study because it is deeply affected by digital platforms as an educational medium and also because it deals mostly with digital natives who use information and communications technology (ICT) for all manner of purposes. Students and teachers are therefore a rich source of User Generated Content (UGC) on social networks and digital platforms. This article shows how useful knowledge can be extracted and visualized from samples of readily-available UGC, in this case tweets from the social network Twitter. The first stage employs topic-modeling using LDA (Latent Dirichlet Allocation) to identify topics, which are then subjected to Sentiment Analysis (SA) using machine-learning (developed in Python). The results take on meaning through an application of data mining techniques and a data visualization algorithm for complex networks. The results obtained show insights related to innovative educational trends that practitioners can use to improve strategies and interventions in the education sector.