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

Author

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  • 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)

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.

Suggested Citation

  • Tserenpurev Chuluunsaikhan & Ga-Ae Ryu & Kwan-Hee Yoo & HyungChul Rah & Aziz Nasridinov, 2020. "Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea," Agriculture, MDPI, vol. 10(11), pages 1-22, October.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:11:p:513-:d:437638
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    References listed on IDEAS

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    Cited by:

    1. Wuyue An & Lin Wang & Yu‐Rong Zeng, 2023. "Text‐based soybean futures price forecasting: A two‐stage deep learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 312-330, March.
    2. Ga-Ae Ryu & Tserenpurev Chuluunsaikhan & Aziz Nasridinov & HyungChul Rah & Kwan-Hee Yoo, 2023. "SCE-LSTM: Sparse Critical Event-Driven LSTM Model with Selective Memorization for Agricultural Time-Series Prediction," Agriculture, MDPI, vol. 13(11), pages 1-21, October.
    3. Xiaohong Yu & Bin Liu & Yongzeng Lai, 2024. "Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications," Sustainability, MDPI, vol. 16(4), pages 1-26, February.
    4. Khishigsuren Davagdorj & Ling Wang & Meijing Li & Van-Huy Pham & Keun Ho Ryu & Nipon Theera-Umpon, 2022. "Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering," IJERPH, MDPI, vol. 19(10), pages 1-21, May.

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