Abstract
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 10 repetitions, all feature sets except the likeability ratings yielded a 14-15% improvement in testing set performance over the base model. Treating the class imbalance with synthetic minority oversampling (SMOTE) increased validation set performance by 24-34% 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.
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 10 repetitions, all feature sets except the likeability ratings yielded a 14-15% improvement in testing set performance over the base model. Treating the class imbalance with synthetic minority oversampling (SMOTE) increased validation set performance by 24-34% 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.
| Original language | English |
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| Publication status | Accepted/In press - 2020 Apr 2 |
| Event | International Conference on Artificial Intelligence in Information and Communication - Takakura Hotel, Fukuoka, Japan Duration: 2020 Feb 19 → 2020 Feb 21 Conference number: 2 http://icaiic.org/ |
Conference
| Conference | International Conference on Artificial Intelligence in Information and Communication |
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| Abbreviated title | ICAIIC |
| Country/Territory | Japan |
| City | Fukuoka |
| Period | 20/2/19 → 20/2/21 |
| Internet address |