Estimation of voting behavior in election using support vector machine, extreme learning machine and deep learning - Tanyildizi, Nural Imik; Tanyildizi, Harun (Springer London Ltd, 2022)
In this study, the extreme learning machine, support vector machine, and deep learning models were used to predict the effect on the voting behavior of election music's used in the elections in Turkey. A questionnaire was conducted to measure the effect of election music on the voting behavior in Turkey. This questionnaire was applied face to face to 412 people. The twelve questions were used to determine the effect of the election music on the voters. The first eleven of these questions in the questionnaire was used as the input variables of the models. The last question was determined whether the election music affects the voting behavior or not. The last question was selected as the output variable of the models. The extreme learning machine, support vector machine, and deep learning models estimated the voting behavior with 98.32%, 92.02% and 99.08% accuracy, respectively. Moreover, the sensitivity analysis and t-test were performed for the input variables in the models. These analyses obtained that 7th question was the most important question on the questionnaire. This study found that politicians can be used the deep learning algorithm to estimate the voting behavior in elections.
Keywords: Election; Voting behavior; Machine learning; Deep learning; Sensitivity analysis; t-test