It is believed that the huge amount of information delivered to the consumers through

mass media, including television and social networks, may affect consumers¡¯ behavior. The purpose

of this study was to forecast the amount required to purchase pork belly meat by using unstructured

data such as broadcast news, TV programs/shows and social network as well as structured data

such as consumer panel data, retail and wholesale prices and production outputs in order to prove

that mass media data release can occur ahead of actual economic activities and consumer behavior

can be predicted by using these data. By using structured and unstructured data from 2010 to 2016

and five forecasting algorithms (autoregressive exogenous model and vector error correction model

for time series, gradient boosting and random forest for machine learning, and long short-term

memory for recurrent neural network), the amounts required to purchase pork belly meat in 2017

were forecasted and compared with the actual amounts to validate model accuracy. Our findings

suggest that when unstructured data were combined with structured data, the forecast pattern is

improved. To date, our study is the first report that forecasts the demand of pork meat by using

structured and unstructured data.