Review of the use of text mining in food price prediction and food security models: A Semantic AI approach
Year: 2025
Type: article
Study design: Systematic Review
Abstract: The increasing volatility of food markets requires more responsive and accurate forecasting models to support effective policy decisions and ensure adaptation to the market. While traditional forecasting methods in agriculture rely heavily on structured quantitative data, they often overlook the latent predictive signals embedded in unstructured textual sources such as news, social media posts, government reports, and expert insights. Same, for food security value chain that covers primarily food production, distribution, and consumption, text mining techniques can help identify trends, sentiment polarity, political and market directions. This paper systematically reviews research in the domain of integrating text mining techniques with quantitative food price prediction and food security models. We conduct a structured literature search across multiple academic databases, applying inclusion criteria that emphasize methodological transparency, empirical validation, and relevance. To extract dominant research themes, we used generative AI tool to summarize the research papers we collected. Informative summaries were generated. Then we employed a topic modeling approach that builds on sentence-BERT embeddings and k-means clustering. This provided grouping of issues found in the research papers while ensuring rich semantic relevance. Then with the use of generative AI tool, titles were generated for each group, these represent thematic research themes. Results highlight the growing use of LSTM and hybrid forecasting models, topic modelling, sentiment analysis and domain-specific transformers in agri-food modelling. With more emphasis on sentiment analysis for food price forecasting, and more emphasis on information extraction, text clustering and trend analysis for food security. Our findings underscore the potential of text-augmented models to enhance the timeliness, contextual awareness, and accuracy of food price forecasts and food security models.
Source: The Business & Management Review
Authors: Ghada Alaa, Ghada Refaat El Said
Institution: Future University in Egypt
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4414653502
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