Predicting Probability of Investment Based on Investor’s Facial Expression in a Startup Funding Pitch

Arya Prabawa; Merel M. Jung; Kostas Stoitsas; Werner Liebregts; Itir Önal Ertuğrul

Predicting Probability of Investment Based on Investor’s Facial Expression in a Startup Funding Pitch

Arya Prabawa, Merel M. Jung, Kostas Stoitsas, Werner Liebregts, Itir Önal Ertuğrul

Research output: Contribution to conference › Paper › Academic

Abstract

Presenting an idea is a critical social interaction, especially
in a startup funding pitch setting where initial investment is at stake.
Understanding a listener’s facial expression can then become extremely
valuable in informing the level of engagement reached by the presenter. Predicting engagement level in other settings, such as an online
study environment, has been explored in previous research, but none
have explored to what extent an investor’s facial expression can predict
the investor’s engagement during a funding pitch and in return predict
the investor’s probability to invest. In this study, we propose to use
Long Short-Term Memory (LSTM) networks along with facial action
units (AUs), facial features extracted with Convolutional Neural Networks (CNN), and the combination of both as features for automated
prediction of probability of investment. The results show a promising
prospect for the proposed LSTM models. Models using CNN features or
combined AU and CNN features outperformed the AU-only model.

Original language	English
Number of pages	11
Publication status	Published - Nov 2022
Event	BNAIC/BeNeLearn 2022: Joint International Scientific Conferences on AI and Machine Learning - Lamot Mechelen, Belgium Duration: 7 Nov 2022 → 9 Nov 2022

Conference

Conference	BNAIC/BeNeLearn 2022
Country/Territory	Belgium
City	Lamot Mechelen
Period	7/11/22 → 9/11/22

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Cite this

@conference{a4a5c4e10fda475fa4558345157431c1,

title = "Predicting Probability of Investment Based on Investor{\textquoteright}s Facial Expression in a Startup Funding Pitch",

abstract = "Presenting an idea is a critical social interaction, especially in a startup funding pitch setting where initial investment is at stake. Understanding a listener{\textquoteright}s facial expression can then become extremely valuable in informing the level of engagement reached by the presenter. Predicting engagement level in other settings, such as an online study environment, has been explored in previous research, but none have explored to what extent an investor{\textquoteright}s facial expression can predict the investor{\textquoteright}s engagement during a funding pitch and in return predict the investor{\textquoteright}s probability to invest. In this study, we propose to use Long Short-Term Memory (LSTM) networks along with facial action units (AUs), facial features extracted with Convolutional Neural Networks (CNN), and the combination of both as features for automated prediction of probability of investment. The results show a promising prospect for the proposed LSTM models. Models using CNN features or combined AU and CNN features outperformed the AU-only model.",

author = "Arya Prabawa and Jung, \{Merel M.\} and Kostas Stoitsas and Werner Liebregts and \{{\"O}nal Ertuğrul\}, Itir",

year = "2022",

month = nov,

language = "English",

note = "BNAIC/BeNeLearn 2022 : Joint International Scientific Conferences on AI and Machine Learning ; Conference date: 07-11-2022 Through 09-11-2022",

}

TY - CONF

T1 - Predicting Probability of Investment Based on Investor’s Facial Expression in a Startup Funding Pitch

AU - Prabawa, Arya

AU - Jung, Merel M.

AU - Stoitsas, Kostas

AU - Liebregts, Werner

AU - Önal Ertuğrul, Itir

PY - 2022/11

Y1 - 2022/11

N2 - Presenting an idea is a critical social interaction, especially in a startup funding pitch setting where initial investment is at stake. Understanding a listener’s facial expression can then become extremely valuable in informing the level of engagement reached by the presenter. Predicting engagement level in other settings, such as an online study environment, has been explored in previous research, but none have explored to what extent an investor’s facial expression can predict the investor’s engagement during a funding pitch and in return predict the investor’s probability to invest. In this study, we propose to use Long Short-Term Memory (LSTM) networks along with facial action units (AUs), facial features extracted with Convolutional Neural Networks (CNN), and the combination of both as features for automated prediction of probability of investment. The results show a promising prospect for the proposed LSTM models. Models using CNN features or combined AU and CNN features outperformed the AU-only model.

AB - Presenting an idea is a critical social interaction, especially in a startup funding pitch setting where initial investment is at stake. Understanding a listener’s facial expression can then become extremely valuable in informing the level of engagement reached by the presenter. Predicting engagement level in other settings, such as an online study environment, has been explored in previous research, but none have explored to what extent an investor’s facial expression can predict the investor’s engagement during a funding pitch and in return predict the investor’s probability to invest. In this study, we propose to use Long Short-Term Memory (LSTM) networks along with facial action units (AUs), facial features extracted with Convolutional Neural Networks (CNN), and the combination of both as features for automated prediction of probability of investment. The results show a promising prospect for the proposed LSTM models. Models using CNN features or combined AU and CNN features outperformed the AU-only model.

M3 - Paper

T2 - BNAIC/BeNeLearn 2022

Y2 - 7 November 2022 through 9 November 2022

ER -

Predicting Probability of Investment Based on Investor’s Facial Expression in a Startup Funding Pitch

Abstract

Conference

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