Exploring Consumer Perception of Food Insecurity Using Big Data

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Abstract
This study investigated consumer perception of food insecurity by refining data collected from social media platforms. Text mining and TF-IDF were used to extract core keywords closely related to food insecurity and analyze their meanings. In addition, time series analysis and sentiment analysis were used to examine temporal and emotional changes. The analysis results showed that keywords, such as health, stress, mental, and depression, appeared frequently, indicating that food insecurity is closely related to psychological and mental problems. In addition, consumers showed high emotional sensitivity to essential nutrients, such as vitamin D, magnesium, calcium, and omega. Furthermore, stress indices and mental and physical response indices increased simultaneously during this period, indicating that food insecurity is a factor that causes emotional and physical responses. The results of the sentiment analysis showed that negative emotions (anxiety, fear, and sadness) were higher than positive emotions, suggesting that discussions related to food insecurity have a negative emotional impact.

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