James A. Cox has been the development manager for SAS Text Miner ever since its inception twelve years ago. Before that, he was one of the initial developers for SAS Enterprise Miner. Jim holds a Ph.D. in Cognitive Psychology and Computer Science from UNC-Chapel Hill, and has published a number of scholarly papers in the areas of psycholinguistics, categorization, and variable selection.
In my last post, I talked about why SAS utilizes a rotated Singular Value Decomposition (SVD) approach for topic generation, rather than using Latent Dirichlet Allocation (LDA). I noted that LDA has undergone a variety of improvements in the last seven years since SAS opted to use the SVD method. So, the
When I talk with more analytically savvy users of SAS® Text Miner or SAS® Contextual Analysis, I inevitably get asked questions about why SAS uses a completely different approach to topic generation than anybody else and why should they trust the approach SAS adopts? These are good questions. I first
The first text analytics product SAS released to the market in 2002 was SAS® Text Miner to enable SAS users to extract insights from unstructured data in addition to structured data. In 2009, in quick succession, SAS released two new products: SAS® Enterprise Content Categorization and SAS® Sentiment Analysis. These
