"Read My Lips": Using Automatic Text Analysis to Classify Politicians by Party and Ideology
This work addresses the problem of analyzing political behavior for researchers and voters, but it is incremental as it applies existing methods to new data with limited improvements.
The study used machine learning on US Congressional speech text to classify senators by party with 70-95% accuracy, but found text-based predictions less accurate than voting data and deteriorated over time.
The increasing digitization of political speech has opened the door to studying a new dimension of political behavior using text analysis. This work investigates the value of word-level statistical data from the US Congressional Record--which contains the full text of all speeches made in the US Congress--for studying the ideological positions and behavior of senators. Applying machine learning techniques, we use this data to automatically classify senators according to party, obtaining accuracy in the 70-95% range depending on the specific method used. We also show that using text to predict DW-NOMINATE scores, a common proxy for ideology, does not improve upon these already-successful results. This classification deteriorates when applied to text from sessions of Congress that are four or more years removed from the training set, pointing to a need on the part of voters to dynamically update the heuristics they use to evaluate party based on political speech. Text-based predictions are less accurate than those based on voting behavior, supporting the theory that roll-call votes represent greater commitment on the part of politicians and are thus a more accurate reflection of their ideological preferences. However, the overall success of the machine learning approaches studied here demonstrates that political speeches are highly predictive of partisan affiliation. In addition to these findings, this work also introduces the computational tools and methods relevant to the use of political speech data.