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- W4387446568 abstract "This work aims to investigate whether displayed body expressions during venture pitches hold predictive value over the success of gathering funding. To this end the performance of several traditional regression models trained on aggregated body expression features is compared to that of deep recurrent models. Moreover, the impact of features and the effect of oversampling to counteract data imbalance are investigated. The results showed that temporal modeling of body expressions with GRU yielded the best performance (average MAE = 16.9). Oversampling was found to not improve models’ performance. The analysis of the impact of features suggest that higher levels of expressed body movements negatively affect investment scores whereas a more open body posture can have a positive effect. These findings show that body expressions can be predictive of investment decisions. Future work will be directed towards integrating these cues into a multimodal model to aid entrepreneurs in improving their pitching skills and further the understanding of the investment decision-making process." @default.
- W4387446568 created "2023-10-10" @default.
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- W4387446568 date "2023-10-09" @default.
- W4387446568 modified "2023-10-11" @default.
- W4387446568 title "Do Body Expressions Leave Good Impressions? - Predicting Investment Decisions based on Pitcher's Body Expressions" @default.
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- W4387446568 doi "https://doi.org/10.1145/3610661.3617156" @default.
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