Gastronomic image in the foodstagrammer's eyes – A machine learning approach

Abstract

Given the rich content that foodstagrammers, people who actively share their dining experiences using photographs and texts on social media, post, they considerably shape a destination's gastronomic image. Using big data analytics, this study examined the formation of gastronomic images from foodstagrammers' perspectives and the associated emotions. Moreover, it demonstrated the applicability of the proposed machine learning approach to evaluate both textual and pictorial content on social media. The study findings extend the current understanding of gastronomic images by identifying the underlying attributes based on the interplay of the three dimensions of food, environment, and activities. Furthermore, the results reveal specific image clusters and dimensions that arouse positive sentiments among foodstagrammers and influence users' engagement with the post. For practitioners, this study provides valuable insights into foodstagrammers' behaviors by identifying the aspects of gastronomic images that effectively arouse interest and engagement, thus promoting gastronomic destinations.