Journal of Information Processing Systems

  • 제14권4호
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  • Pages.904-915
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  • 2018
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Predicting the Unemployment Rate Using Social Media Analysis

  • Ryu, Pum-Mo (Dept. of ICT & Language Processing, School of Southeast Asian Studies, Busan University of Foreign Studies)
  • 투고 : 2017.08.25
  • 심사 : 2017.12.12
  • 발행 : 2018.08.31
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초록

We demonstrate how social media content can be used to predict the unemployment rate, a real-world indicator. We present a novel method for predicting the unemployment rate using social media analysis based on natural language processing and statistical modeling. The system collects social media contents including news articles, blogs, and tweets written in Korean, and then extracts data for modeling using part-of-speech tagging and sentiment analysis techniques. The autoregressive integrated moving average with exogenous variables (ARIMAX) and autoregressive with exogenous variables (ARX) models for unemployment rate prediction are fit using the analyzed data. The proposed method quantifies the social moods expressed in social media contents, whereas the existing methods simply present social tendencies. Our model derived a 27.9% improvement in error reduction compared to a Google Index-based model in the mean absolute percentage error metric.

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참고문헌

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