Exploring consumer perception of edible insects in Korea using text mining

Abstract Food security is a critical issue derived from the limitations of protein production systems, promoting the exploration of edible insects as alternative protein sources. While perception surveys are essential to a research planning process to address entomophobia, they are costly and time-consuming. Analysing text data on social media using text mining has been suggested for innovative perception surveys in the era of big data. This study analysed articles about edible insects from blogs and online communities to understand the Korean public s perception. The comprehensive results of word frequency and similarity analysis showed that Koreans are steadily interested in edible insects as future food and in farms, industries, and businesses of edible insects in Korea. Additionally, the public s perception of health benefits or drying processing methods of edible insects was identified over the years. Results of topic modeling, though less intuitive than word frequency and similarity analysis, provided complementary insights such as future, alternative, health effect, usage, and application of edible insects. The text mining results (TMR) were validated because similar results on TMR and perception survey results (PSR) were obtained. TMR provided insights into unpredictable words or topics, such as domestic markets and health issues, which were not captured by PSR. In conclusion, the public s perception and new meanings of edible insects were derived using text mining, and the applicability of this novel and cost-effective method as a perception survey was confirmed through this research.