Analyzing Online Consumer Reviews on Seafood Products Using Multifaceted Text Mining Techniques

This study aims to identify consumer behavior patterns and derive key purchasing factors and product packaging strategies through multifaceted text mining analysis.By leveraging word cloud analysis, association rule mining, and Latent Dirichlet Allocation (LDA), we analyzed online consumer reviews for the most popular seafood in Korea, such as mackerel, squid, and seaweed.The text mining results revealed that consumers prioritize taste, freshness, convenience, price, quality, delivery, and storage when purchasing.In particular, we identified the purposes for consumers purchasing processed seafood products.Proposed packaging strategies that emphasize freshness and taste, target key buyers, utilize vacuum packaging, individual packaging, and products suitable for frozen storage.Additionally, we suggested enhancing reliability by including quality assurance marks or certificates on the packaging, along with clear information on price and delivery options.By analyzing a vast amount of online consumer reviews, this study overcomes the limitations associated with traditional survey or interview methods, offering a more objective and accurate reflection of consumer opinions.The insights provided by this research are intended to serve as a valuable reference for seafood processing companies, helping them better meet consumer needs and enhance their competitiveness in the market.This paper ultimately contributes to a deeper understanding of consumer preferences and offers strategic guidance for the seafood industry in Korea.