A study of consumers' perceptions of take-out food before and after the COVID-19 outbreak: applying big data analysis

ABSTRACT

This study explored changes in consumers' perceptions of take-out food before and after the onset of the COVID-19 pandemic using big data collected from social media. Using "take-out food" as a keyword, 18,544 search results were found in 2019, before the COVID-19 outbreak, compared to 20,718 search results in 2021. These keywords were analyzed using text mining, semantic network analysis, CONCOR analysis, and sentiment analysis, respectively, to understand consumers' perception of take-out food. Using text mining, in 2019, "dining-out" was the most frequent search term associated with take-out food, followed by packing, famous restaurant, family, delicious, menu, and available. In 2021, "dining-out" was again the most popular keyword, followed by packing, famous restaurant, delivery, family, delicious, available, and Corona. A semantic network analysis showed that, in 2019, four categories emerged delicious, meat, satisfaction, and lunchbox. The same analysis showed that, in 2021, the categories were delicious, meat, good, and home meal. These findings suggest that, after COVID-19, take-out food began to be recognized as a daily meal that can replace home-cooked meals. According to the sentiment analysis, the number of positive keywords decreased by 4.03% after COVID-19, while the number of negative keywords increased at the same rate;regarding the increase in negative keywords, such as sadness, disgust, and fear, since the emergence of COVID-19, consumers' anxiety about eating out due to the virus was observed. This study can be useful by providing core data and an analysis method necessary for food service companies' business activities and decision making related to take-out amid consumers' rapidly changing needs for the dining-out environment caused by COVID-19.