  Political science, and social science in general, have traditionally been
using computational methods to study areas such as voting behavior, policy
making, international conflict, and international development. More recently,
increasingly available quantities of data are being combined with improved
algorithms and affordable computational resources to predict, learn, and
discover new insights from data that is large in volume and variety. New
developments in the areas of machine learning, deep learning, natural language
processing (NLP), and, more generally, artificial intelligence (AI) are opening
up new opportunities for testing theories and evaluating the impact of
interventions and programs in a more dynamic and effective way. Applications
using large volumes of structured and unstructured data are becoming common in
government and industry, and increasingly also in social science research. This
chapter offers an introduction to such methods drawing examples from political
science. Focusing on the areas where the strengths of the methods coincide with
challenges in these fields, the chapter first presents an introduction to AI
and its core technology - machine learning, with its rapidly developing
subfield of deep learning. The discussion of deep neural networks is
illustrated with the NLP tasks that are relevant to political science. The
latest advances in deep learning methods for NLP are also reviewed, together
with their potential for improving information extraction and pattern
recognition from political science texts.
