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Our study analyzed the impact of African swine fever (ASF) news on the Korean meat market using sentiment analysis. We applied a neural network language model (NNLM) to generate a sentiment index indicating whether the news had a positive or negative impact on consumer expectations. We analyzed 24,143 news articles to estimate the impulse responses of meat price variables to sentiment shocks. Our study contributes significantly to agricultural economics as it applies NNLM to generate a sentiment index. The empirical results indicated that ASF news sentiment has a substantial impact on meat prices in Korea, and there is evidence of substitution effects among different types of meat. ASF news has a positive impact on the price of pork, negative effects on beef and chicken prices, and a greater impact on the price of chicken than beef. The findings imply that the effect of ASF news on demand outweighs its impact on supply in the pork market, whereas the effect on supply surpasses the effect on demand in the beef and chicken market. We believe our methods and results will inspire discussions among applied economists studying consumer behavior in this specific market and could encourage the application of big data analysis to the agricultural economy.本研究借助情感分析技术，探究了非洲猪瘟（African Swine Fever, ASF）相关新闻对韩国肉类市场的影响。本研究采用神经网络语言模型（Neural Network Language Model, NNLM）构建情感指数，以衡量相关新闻对消费者预期的正向或负向影响。我们共分析了24143篇新闻报道，以评估肉类价格变量对情感冲击的脉冲响应。本研究通过将神经网络语言模型应用于情感指数构建，为农业经济学领域作出了重要贡献。实证结果表明，非洲猪瘟相关新闻的情感倾向对韩国肉类价格具有显著影响，且不同肉类品类间存在替代效应。非洲猪瘟相关新闻对猪肉价格具有正向影响，对牛肉与鸡肉价格则产生负向影响，且对鸡肉价格的影响幅度大于牛肉。研究结果显示，在猪肉市场中，非洲猪瘟相关新闻对需求的影响超过其对供给的影响；而在牛肉与鸡肉市场中，供给端受到的影响则大于需求端。我们认为，本研究的方法与结论将为研究该特定市场消费者行为的应用经济学家提供研究思路，并有望推动大数据分析在农业经济领域的应用。