Sentiment analysis on the people's daily

Abstract

We propose a semi-supervised bootstrapping algorithm for analyzing China's foreign relations from the People's Daily. Our approach addresses sentiment target clustering, subjective lexicons extraction and sentiment prediction in a unified framework. Different from existing algorithms in the literature, time information is considered in our algorithm through a hierarchical bayesian model to guide the bootstrapping approach. We are hopeful that our approach can facilitate quantitative political analysis conducted by social scientists and politicians.