Predicting the Unemployment Rate Using Social Media Analysis

Pum-Mo Ryu
Volume: 14, No: 4, Page: 904 ~ 915, Year: 2018
10.3745/JIPS.04.0079
Keywords: Google Index, Prediction, Sentiment Analysis, Social Media, Unemployment Rate
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Abstract
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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Cite this article
IEEE Style
Pum-Mo Ryu, "Predicting the Unemployment Rate Using Social Media Analysis," Journal of Information Processing Systems, vol. 14, no. 4, pp. 904~915, 2018. DOI: 10.3745/JIPS.04.0079.

ACM Style
Pum-Mo Ryu, "Predicting the Unemployment Rate Using Social Media Analysis," Journal of Information Processing Systems, 14, 4, (2018), 904~915. DOI: 10.3745/JIPS.04.0079.