© 2019 IEEE.Politics is an area of broad interest to policy-makers, researchers, and the general public. The recent explosion in the availability of electronic data and advances in data analysis methods - including techniques from machine learning - have led to many studies attempting to extract political insight from this data. Speeches in the U.S. Congress represent an exceptionally rich dataset for this purpose, and these have been analyzed by many researchers using statistical and machine learning methods. In this paper, we analyze House of Representatives floor speeches from the 1981 - 2016 period, with the goal of inferring the partisan affiliation of the speakers from their use of words. Previous studies with sophisticated machine learning models has suggested that this task can be accomplished with an accuracy in the 55 to 80% range, depending on the year. In this paper, we show that, in fact, very comparable results can be obtained using a much simpler linear classifier in word space, indicating that the use of words in partisan ways is not particularly complicated. Our results also confirm that, over the period of study, it has become steadily easier to infer partisan affiliation from political speeches in the United States. Finally, we make some observations about specific terms that Republicans and Democrats have favored over the years in service of partisan expression.