Predicting Political Tendency of Posts on Facebook

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.