Polarity Classification of Public Health Opinions in Chinese

Authors:
Changli Zhang;Daniel Zeng;Qingyang Xu;Xueling Xin;Wenji Mao;Fei-Yue Wang
Affiliations:
College of Computer Science and Technology, Jilin University, China and Artillery Command College of Shenyang, , China;Institute of Automation, Chinese Academy of Sciences, and MIS Department, University of Arizona,;College of Computer Science and Technology, Jilin University, China;College of Computer Science and Technology, Jilin University, China;Institute of Automation, Chinese Academy of Sciences,;Institute of Automation, Chinese Academy of Sciences,
Venue:
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Year:
2008

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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.