Media reporting shapes public opinion which can in turn influence events, particularly in political elections, in which candidates both respond to and shape public perception of their campaigns. We use computational linguistics to automatically predict the impact of news on public perception of political candidates. Our system uses daily newspaper articles to predict shifts in public opinion as reflected in prediction markets. We discuss various types of features designed for this problem. The news system improves market prediction over baseline market systems.
REF automatically predicted the impact of news on public perception of political candidates.
Reading the Markets: Forecasting Public Opinion of Political Candidates by News Analysis
International Conference On Computational Linguistics
Computer Science