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Related Experiment Video

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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Published on: August 9, 2024

Automatic Estimation of Multidimensional Personality From a Single Sound-Symbolic Word.

Maki Sakamoto¹, Junji Watanabe², Koichi Yamagata¹

¹Department of Informatics, The University of Electro-Communications, Chofu, Japan.

Frontiers in Psychology

|May 10, 2021

Summary

This study introduces a novel system to estimate personality traits using a single Japanese sound-symbolic word (SSW), reducing participant burden. The method accurately predicts personality dimensions, offering a new tool for personality computing.

Keywords:

Japanese onomatopoeia personality evaluation sound-symbolic word system construction

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Area of Science:

Psychology
Computational Linguistics
Artificial Intelligence

Background:

The
big five
personality traits (Extroversion, Agreeableness, Conscientiousness, Neuroticism, Openness) are standard but require lengthy questionnaires.
Current personality evaluation methods are time-consuming and burdensome for participants.

Purpose of the Study:

To develop an efficient method for estimating multidimensional personality traits using only a single word.
To leverage Japanese sound-symbolic words (SSWs) for intuitive personality expression.
To reduce the participant burden associated with traditional personality assessments.

Main Methods:

Developed a system to convert Japanese SSWs into 50 personality-related adjective pairs.
Utilized the inherent sound-meaning association in SSWs to capture personality nuances.
Evaluated the system's prediction accuracy using multiple correlation coefficients and a user rating experiment.

Main Results:

The system achieved multiple correlation coefficients exceeding 0.75 for 48 personality-related adjective pairs, explaining over 50% of data variance.
User evaluation showed an average appropriateness score of 1.25 on a seven-point scale for 50 personality-related adjective pairs.
The system demonstrated significant accuracy in estimating personality traits from single SSWs.

Conclusions:

The proposed system offers an effective and efficient alternative for personality evaluation.
This method can significantly reduce participant burden in personality assessments.
The system holds potential for advancing personality computing, particularly in evaluation and communication applications.