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Election vote share prediction using a sentiment-based fusion of Twitter data with Google trends and online polls
, Article 6th International Conference on Data Science, Technology and Applications, DATA 2017, 24 July 2017 through 26 July 2017 ; 2017 , Pages 363-370 ; 9789897582554 (ISBN) ; Modirshanechi, A ; Aghajan, H. K ; Institute for Systems and Technologies of Information, Control and Communication (INSTICC) ; Sharif University of Technology
SciTePress
2017
Abstract
It is common to use online social content for analyzing political events. Twitter-based data by itself is not necessarily a representative sample of the society due to non-uniform participation. This fact should be noticed when predicting real-world events from social media trends. Moreover, each tweet may bare a positive or negative sentiment towards the subject, which needs to be taken into account. By gathering a large dataset of more than 370,000 tweets on 2016 US Elections and carefully validating the resulting key trends against Google Trends, a legitimate dataset is created. A Gaussian process regression model is used to predict the election outcome; we bring in the novel idea of...