Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/10/11/513 |
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| author | Tserenpurev Chuluunsaikhan Ga-Ae Ryu Kwan-Hee Yoo HyungChul Rah Aziz Nasridinov |
| author_facet | Tserenpurev Chuluunsaikhan Ga-Ae Ryu Kwan-Hee Yoo HyungChul Rah Aziz Nasridinov |
| author_sort | Tserenpurev Chuluunsaikhan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:12:36Z |
| format | Article |
| id | doaj.art-0d5eb95ff58943a3964580eccb531b66 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-10T15:12:36Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-0d5eb95ff58943a3964580eccb531b662023-11-20T19:12:21ZengMDPI AGAgriculture2077-04722020-10-01101151310.3390/agriculture10110513Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South KoreaTserenpurev Chuluunsaikhan0Ga-Ae Ryu1Kwan-Hee Yoo2HyungChul Rah3Aziz Nasridinov4Department of Computer Science, Chungbuk National University, Cheongju 28644, KoreaDepartment of Computer Science, Chungbuk National University, Cheongju 28644, KoreaDepartment of Computer Science, Chungbuk National University, Cheongju 28644, KoreaDepartment of Management Information System, Chungbuk National University, Cheongju 28644, KoreaDepartment of Computer Science, Chungbuk National University, Cheongju 28644, KoreaKnowing 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.https://www.mdpi.com/2077-0472/10/11/513agri-foodlivestock pricepork priceprice forecasttopic modelingLSTM forecast |
| spellingShingle | Tserenpurev Chuluunsaikhan Ga-Ae Ryu Kwan-Hee Yoo HyungChul Rah Aziz Nasridinov Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea Agriculture agri-food livestock price pork price price forecast topic modeling LSTM forecast |
| title | Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea |
| title_full | Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea |
| title_fullStr | Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea |
| title_full_unstemmed | Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea |
| title_short | Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea |
| title_sort | incorporating deep learning and news topic modeling for forecasting pork prices the case of south korea |
| topic | agri-food livestock price pork price price forecast topic modeling LSTM forecast |
| url | https://www.mdpi.com/2077-0472/10/11/513 |
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