Modeling and Forecasting Elections Using Topic Models

dc.contributor.advisorHeskes, T.M.
dc.contributor.advisorVuurpijl, L.G.
dc.contributor.authorBerkel, B.M. van
dc.description.abstractAfter elections, people want to know who will win the election as quickly as possible. During election night forecasts are made, based on both polls and the results from early polling stations. In this research a forecasting model based on topic models is proposed. The model's forecasting performance is compared to a linear model's performance using the Dutch House of Representatives election. The model is also used to visualize and analyze voting profiles. The proposed model outperforms existing linear models with a lower mean absolute error if 10% or more of the polling stations are observed. For 2.5% or less observed polling stations the linear model has a lower mean absolute error. The proposed model is also able to give insight into voting behavior by visualizing voter pro les. Thus, the proposed model is useful for both forecasting and modeling elections.en_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.titleModeling and Forecasting Elections Using Topic Modelsen_US
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