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This paper analyzes how candidate choice prediction improves by differentpsychological predictors. To investigate this question, it collected anoriginal survey dataset featuring the popular TV series "Game of Thrones". Therespondents answered which character they anticipated to win in the finalepisode of the series, and explained their choice of the final candidate infree text from which sentiments were extracted. These sentiments were comparedto feature sets derived from candidate likeability and candidate personalityratings. In our benchmarking of 10-fold cross-validation in 100 repetitions,all feature sets except the likeability ratings yielded a 10-11% improvement inaccuracy on the holdout set over the base model. Treating the class imbalancewith synthetic minority oversampling (SMOTE) increased holdout set performanceby 20-34% but surprisingly not testing set performance. Taken together, ourstudy provides a quantified estimation of the additional predictive value ofpsychological predictors. Likeability ratings were clearly outperformed by thefeature sets based on personality, emotional valence, and basic emotions.

Who Wins the Game of Thrones? How Sentiments Improve the Prediction of Candidate Choice