Russ Albright is a research statistician developer at SAS. Over the past 15 years, his text mining work has involved algorithm development, statistical modeling, and high performance computing. He holds a PhD in Applied Mathematics from Clemson University and enjoys helping customers use SAS software on their text analytics problems.
In an upcoming paper for SAS Global Forum, several of us from the SAS Text Analytics team explore shifting the context of our underlying representation from documents to the sentences that are within the documents. We then look at how this shift can allow us to answer new text mining
Recently, I have been thinking about how search can play more of a part in discovery and exploration with SAS Text Miner. Unsupervised text discovery usually begins with a look at the frequent or highly weighted terms in the collection, perhaps includes some edits to the synonym and stop lists,
A new version of SAS® Text Miner and SAS® High-Performance Text Mining has recently been made available and I want to demonstrate some of the performance improvements that can be gained with this release. I’ll use a topic analysis that discovers the main themes in a document collection and consists
