Difference between revisions of "Park et al CSCW 2011. The Politics of Comments: Predicting Political Orientation of News Stories with Commenters’ Sentiment Patterns"
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== The main idea == | == The main idea == | ||
| − | + | Though it is a difficult problem to analyze the political orientation of a news article by computational analysis, however there exists commenters with clear political views and they are most likely to present the same views consistently towards various political issues. By identifying predictive commenters (who show a high degree of regularity in their sentiment patterns) and analyzing their sentiments of comments, the political orientation of the news article is deduced. When the comment is negative, the article’s political orientation can be predicted to be the opposite from that of the commenters; when the comment is positive, it can be predicted to be the same as that of the commenter. | |
Revision as of 17:41, 1 October 2012
Citation
Souneil Park, Minsam Ko, Jungwoo Kim, Ying Liu, and Junehwa Song.“The Politics of Comments: Predicting Political Orientation of News Stories with Commenters’ Sentiment Patterns”, in Proceedings of the 2011 ACM Conference on Computer Supported Cooperative Work (CSCW 2011).
Online Version
Summary
This Paper tries to predict the political orientation of news articles by analyzing the sentiment patterns of commenters. It is difficult to interpret the political orientation of a news article by computation analysis of the text or metadata since they cover complex political discourse such as party, government, economy etc. This paper presents a new "social annotation analysis" approach of predicting the political orientation of news articles.
The main idea
Though it is a difficult problem to analyze the political orientation of a news article by computational analysis, however there exists commenters with clear political views and they are most likely to present the same views consistently towards various political issues. By identifying predictive commenters (who show a high degree of regularity in their sentiment patterns) and analyzing their sentiments of comments, the political orientation of the news article is deduced. When the comment is negative, the article’s political orientation can be predicted to be the opposite from that of the commenters; when the comment is positive, it can be predicted to be the same as that of the commenter.
