2020/02/29 by Chaehan So, So, Chaehan
Computer Science · Decision Sciences · Psychology · Social Sciences · #Decision-Making and Behavioral Economics #Evolutionary Psychology and Human Behavior #Social and Intergroup Psychology #cs.CL #cs.CY
paper · pdf · doi:10.48550/arxiv.2003.07683
To be published in IEEE conference proceedings: International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
arxiv created 2020/02/29 · arxiv updated 2020/03/18
This paper analyzes how candidate choice prediction improves by different psychological predictors. To investigate this question, it collected an original survey dataset featuring the popular TV series "Game of Thrones". The respondents answered which character they anticipated to win in the final episode of the series, and explained their choice of the final candidate in free text from which sentiments were extracted. These sentiments were compared to feature sets derived from candidate likeability and candidate personality ratings. In our benchmarking of 10-fold cross-validation in 100 repetitions, all feature sets except the likeability ratings yielded a 10-11% improvement in accuracy on the holdout set over the base model. Treating the class imbalance with synthetic minority oversampling (SMOTE) increased holdout set performance by 20-34% but surprisingly not testing set performance. Taken together, our study provides a quantified estimation of the additional predictive value of psychological predictors. Likeability ratings were clearly outperformed by the feature sets based on personality, emotional valence, and basic emotions.