Polarity classification of public health opinions in Chinese

Abstract

Public health events with major consequences are occurring globally. Increasingly people are expressing their views on these events and government agencies' responses and policies online. Recent years have seen significant interest in investigating methods to recognize favorable and unfavorable sentiments towards specific subjects, including public health opinions, from online natural language text. However, most of these efforts are focused on English. In this paper, we study Chinese opinion mining in the context of public health opinions. We explore two complementary approaches-a Chinese opinionated word-based approach and a machine learning approach. We also conduct related comparative analysis and discuss the important role Chinese NLP techniques play in polarity classification. © 2008 Springer-Verlag Berlin Heidelberg.