Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea

Tserenpurev Chuluunsaikhan, Ga-Ae Ryu, Kwan-Hee Yoo, HyungChul Rah and Aziz Nasridinov
Additional contact information
Tserenpurev Chuluunsaikhan: Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea
Ga-Ae Ryu: Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea
Kwan-Hee Yoo: Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea
HyungChul Rah: Department of Management Information System, Chungbuk National University, Cheongju 28644, Korea
Aziz Nasridinov: Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea

Agriculture, 2020, vol. 10, issue 11, 1-22

Abstract: Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.

Keywords: agri-food; livestock price; pork price; price forecast; topic modeling; LSTM forecast (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2077-0472/10/11/513/pdf (application/pdf)
https://www.mdpi.com/2077-0472/10/11/513/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:10:y:2020:i:11:p:513-:d:437638

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().