During the last year of a presidential term in the United States of America, the race to the White House has everybody excited. News channels and newspapers provide “expert” analysis of day to day events. However, many of the expert opinions are biased and reflect a commentator’s political viewpoint or affiliation. Can text mining be used to look at the data objectively and cut through the political rhetoric? In this talk, a script of the 1st 2016 presidential debate is analyzed with SAS Text Miner. Words are counted and stemmed, documents are grouped into clusters, topics are identified and candidates are analyzed while trying to determine what separates one candidate from the other.