topic modeling

Words Versus Meanings

There is a significant hurdle that researchers face when considering the addition of qualitative methods to the-suntheir research designs.  This has to do with the analysis – making sense – of the qualitative data.  One could argue that there are certainly other hurdles that lie ahead, such as those related to a quality approach to data collection, but the greatest perceived obstacle seems to reside in how to efficiently analyze qualitative outcomes.  This means that researchers working in large organizations that hope to conduct many qualitative studies over the course of a year are looking for a relatively fast and inexpensive analysis solution compared to the traditionally more laborious thought-intensive efforts utilized by qualitative researchers.

Among these researchers, efficiency is defined in terms of speed and cost.  And for these reasons they gravitate to text analytic programs and models powered by underlying algorithms.  The core of modeling solutions – such as word2vec and topic modeling – rests on “training” text corpora to produce vectors or clusters of co-occurring words or topics.  There are any number of programs that support these types of analytics, including those that incorporate data visualization functions that enable the researcher to see how words or topics congregate (or not), producing images such as these Read Full Text