Title: A Model for Measuring Emotion in Political Speech with Audio Data

Abstract: Though we generally assume otherwise, humans communicate using more than bags of words alone.  Auditory and visual cues convey important information, such as emotion, in many phenomena of interest of political scientists. However, in part due to the relative difficulty of processing audio data, research has disproportionately focused on the textual component of pre-transcribed corpora.  We develop a new hidden Markov model for emotional analysis to complement and extend existing methods for text analysis.  The tools and model are applied to oral arguments in the Supreme Court and Presidential campaign speeches.