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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/118483

Title:	Predicting Political Tendency of Posts on Facebook
Authors:	邱淑怡 Chiu, Shu-I 徐國偉 Hsu, Kuo-Wei
Contributors:	資科博七
Keywords:	Text mining;Facebook
Date:	2018-02
Issue Date:	2018-07-09 14:50:18 (UTC+8)
Abstract:	Facebook is the most popular social networking website. Every post on Facebook actually can imply the user‟s emotion or opinion. In this paper, we present our analysis on posts associated with left- and right-wing politics in the United States of America. Our dataset contains posts several related Facebook fan pages. We analyze sentiment of posts for the prediction of left- or right-wing politics. We build sentiment features for the prediction and evaluate prediction performance. The results show that F1-score can be as high as 0.95 when TF-IDF is used with a decision tree. Posts generally involve emotional words. We use the lexical databases for sentiment analysis. Our experiment results show that the sentiment analysis is sensitive to some classification algorithms.
Relation:	International Conference on Software and Computer Applications, Universiti Malaysia Pahang
Data Type:	conference
DOI link:	http://dx.doi.org/10.1145/3185089.3185094
DOI:	10.1145/3185089.3185094
Appears in Collections:	[Department of Computer Science ] Proceedings

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