Methods covered in the "Text Algorithms" section: 1) preliminaries/ pre-processing 2) bag-of-words representation of docs 3) dimensionality reduction, e.g. topic models 4) word embeddings with local context 5) embedding sequences with attention 6) supervised learning
It’s no wonder then that social scientists are increasingly interested in narratives -- the stories we tell in fiction, politics, and life -- and how they shape beliefs, behavior, and government policies.
“In politics, when reason and emotion collide, emotion invariably wins.” -- Drew Westen, The Political Brain.
Interesting idea! What do the data say about that?
New working paper: "Emotion and Reason in Political Language", with @gloriagennaro.
PDF: bit.ly/gennaro-ash-em…
We use computational linguistics tools ("word embeddings") to map out a dimension for emotion on one pole and cognition on another pole.