Post provided by Gabriela Nunez-Mir
A search of almost any topic on Google Scholar promises to return tens of thousands of hits in less than a second. The first step in any research endeavour is to wade through the titanic amounts of articles available to become acquainted with the existing knowledge. For many people it’s one of the most dreadful and tedious parts of the scientific process.
But what if we could streamline/facilitate this step by automatizing parts of it? Automated content analysis (ACA) gives us the opportunity to do just that. ACA – a text-mining method that uses text-parsing and machine learning – is able to classify vast amounts of text into categories of named concepts. It can then quantify the frequency of those concepts and the relationships among them. Continue reading