Abstract
In this paper, we explore the use of sentiment analysis of influential messages on social media to improve political election forecasting. While social media users are not necessarily representative of the overall electors, bias correction of users messages is critical for producing a reliable forecast. The observation motivates our work is that people on social media consult the messages of each other before taking a decision, this means that social media users influence each other. We first built a classifier to detect politically influential messages based on different aspects (messages content, time, sentiment, and emotion). Then, we predicted electoral candidates votes using sentiment degree of influential messages. We applied our proposed model to the 2016 United States presidential election. We conducted experiments at different intervals of times. Results show that our approach achieves better performance than both off-line polling and classical approaches.
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Oueslati, O., Hajhmida, M.B., Ounelli, H., Cambria, E. (2023). Sentiment Analysis of Influential Messages for Political Election Forecasting. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_21
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