Predicting Political Tendency of Posts on Facebook

被引：10

作者：

Chiu, Shu-, I ^{[1
]}

Hsu, Kuo-Wei ^{[1
]}

机构：

[1] Natl Chengchi Univ, 64,Sec 2,Zhi Nan Rd, Taipei 11605, Taiwan

来源：

PROCEEDINGS OF 2018 7TH INTERNATIONAL CONFERENCE ON SOFTWARE AND COMPUTER APPLICATIONS (ICSCA 2018) | 2018年

关键词：

Text mining; Facebook;

D O I：

10.1145/3185089.3185094

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：110 / 114

页数：5

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