Hamid Hassanpour - Prof. Hamid Hassanpour Shahrood University of Technology Faculty of Computer Engineering and IT ۰۹۱۱ ۱۱۲ ۸۳۸۰ h_hassanpour@yahoo.com h.hassanpour@shahroodut.ac.ir
Abulfazl Yavari - Shahrood University of Technology
Bagher Rahimpour - Mazandaran University of Science and Technology
Mehregan Mahdavi - Sydney International School of Technology and Commerce, Sydney, Australia

Election prediction has always been of interest to many people. In the last decade, the increasing influence of social networks and the possibility of sharing opinions and ideas have rendered election prediction based on social network data analysis. This paper, drawing on Twitter data and sentiment analysis, uses the proportion of positive messages rate to negative messages rate as an effective indicator for predicting elections. Then, using the aging estimation method, it predicts the values of this indicator in future time windows. The experiments conducted on the Twitter data related to the ۲۰۲۰ United States presidential election in a four-month time window indicate that the indicator values and eventually the election results can be predicted with high accuracy. The experiments conducted on the Twitter data related to the ۲۰۲۰ United States presidential election in a four-month time window indicate that the indicator values and eventually the election results can be predicted with high accuracy.

Virtual Social Network, Twitter Data Analysis, Aging Estimation Method