Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Group Emotion Prediction System based on Modular Bayesian Networks

모듈형 베이지안 네트워크 기반 대중 감성 예측 시스템

  • 최슬기 (연세대학교 인지과학협동과정) ;
  • 조성배 (연세대학교 컴퓨터과학과)
  • Received : 2017.02.08
  • Accepted : 2017.09.01
  • Published : 2017.11.15
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Abstract

Recently, with the development of communication technology, it has become possible to collect various sensor data that indicate the environmental stimuli within a space. In this paper, we propose a group emotion prediction system using a modular Bayesian network that was designed considering the psychological impact of environmental stimuli. A Bayesian network can compensate for the uncertain and incomplete characteristics of the sensor data by the probabilistic consideration of the evidence for reasoning. Also, modularizing the Bayesian network has enabled flexible response and efficient reasoning of environmental stimulus fluctuations within the space. To verify the performance of the system, we predict public emotion based on the brightness, volume, temperature, humidity, color temperature, sound, smell, and group emotion data collected in a kindergarten. Experimental results show that the accuracy of the proposed method is 85% greater than that of other classification methods. Using quantitative and qualitative analyses, we explore the possibilities and limitations of probabilistic methodology for predicting group emotion.

최근 통신 기술의 발달로 공간 내 환경 자극을 나타내는 다양한 센서 데이터 수집이 가능해졌다. 베이지안 네트워크는 추론 근거를 확률적으로 고려함으로써 센서 데이터의 불확실하고 불완전한 특성을 보완할 수 있다. 본 논문은 환경 자극의 심리적 영향력을 고려하여 설계된 모듈형 베이지안 네트워크 기반 대중 감성 예측 시스템을 제안한다. 또한 단일 베이지안 네트워크를 모듈화하여 공간 내 환경 자극 변동의 유연한 대응 및 효율적 추론을 수행하였다. 시스템의 성능 검증을 위해 유치원 공간에서 수집된 조도, 음량, 온도, 습도, 색 온도, 음향, 향기, 대중 감성 데이터를 기반으로 대중 감성을 예측하였다. 실험 결과, 제안하는 방법의 예측 정확도는 85%로 여타 분류 기법보다 높은 성능을 나타내었다. 정량적, 정성적 분석을 통해 대중 감성 예측을 위한 확률 기반 방법론의 가능성 및 한계를 분석하였다.

Keywords

Acknowledgement

Supported by : 정보통신기술진흥센터

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