Sentiment Analysis on the People’s Daily

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 467–476, October 25-29, 2014, Doha, Qatar. c⃝2014 Association for Computational Linguistics Sentiment Analysis on the People’s Daily Jiwei Li1 and Eduard Hovy2 1Computer Science Department, Stanford University, Stanford, CA 94305, USA 2Language Technology Institute, Carnegie Mellon University, PA 15213, USA jiweil@stanford.edu ehovy@andrew.cmu.edu Abstract We propose a semi-supervised bootstrap- ping algorithm for analyzing China’s for- eign relations from the People’s Daily. Our approach addresses sentiment tar- get clustering, subjective lexicons extrac- tion and sentiment prediction in a unified framework. Different from existing algo- rithms 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 quantita- tive political analysis conducted by social scientists and politicians. 1 Introduction “We have no permanent allies, no permanent friends, but only permanent interests.” -Lord Palmerston Newspapers, especially those owned by official governments, e.g., Pravda from Soviet Union, or People’s Daily from P.R. China, usually pro- vide direct information about policies and view- points of government. As national policies change over time, the tone that newspapers adopt, es- pecially sentiment, changes along with the poli- cies. For example, there is a stark contrast be- tween the American newspapers’ attitudes towards Afghanistan before and after 911. Similarly, con- sider the following examples extracted from the People’s Daily1: • People’ Daily, Aug 29th, 1963 All those who are being oppressed and exploited, Unite !! Beat US Imperialism and its lackeys. • People’s Daily, Oct, 20th, 2002 A healthy, steady and developmental relationship be- tween China and US, conforms to the fundamental in- terests of people in both countries, and the trend of his- torical development. 1Due to the space constraints, we only show the translated version in most of this paper. Automatic opinion extraction from newspapers such as people’s daily can facilitate sociologists ’or political scientists’ research or help political pundits in their decision making process. While our approach applies to any newspaper in princi- ple, we focus here on the People’s Daily2 (Renmin Ribao), a daily official newspaper in the People’s Republic of China. While massive number of works have been in- troduced in sentiment analysis or opinion target extraction literature (for details, see Section 2), a few challenges limit previous efforts in this spe- cific task: First, the heavy use of linguistic phe- nomenon in the People’s Daily including rhetoric, metaphor, proverb, or even nicknames, makes ex- isting approaches less effective for sentiment in- ference as identifying these expressions is a hard NLP problem in nature. Second, as we are more interested in the degree of sentiment rather than binary classification (i.e., positive versus negative) towards an entity (e.g. country or individual) in the news article, straight- forward algorithms to apply would be document- level sentiment analysis approaches such as vector machine/regression (Pang et al., 2002) or super- vised LDA (Blei and McAuliffe, 2010). A single news article, usually contains different attitudes towards multiple countries …

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