Exploring the Key Design Attributes of Cafés from User-Generated Contents - A Case Study of Two Cafés of Unique Interior Designs in Yeonnam-dong, Seoul -

In the last couple of decades, the use of social media has consistently increased. Different social media platforms allow users to interact and create diverse types of content, making them able to share their opinions. With brands getting involved in these platforms, it is important to know how their image is portrayed by their customers on social media. This includes the café industry. The purpose of our study is twofold. One is to understand which design attributes make a café appealing to social media users, and another is to examine whether the Google Cloud Vision API is able to differentiate the key design attributes using user-generated content, which are Instagram posts in this research. In order to achieve these aims, we conducted a comparative case study using two cafés of unique interior designs located in Yeonnam-dong in Seoul, South Korea. Specifically, Greem Cafe and Perception Coffee were used. Using these cases as hashtags, we scraped the posts using a Python web scraper and screened out the posts irrelevant to the cafés. After, we ran them through the Google Cloud Vision API to obtain the labels and screened out the labels irrelevant to the interior designs, such as people and amenities. Finally, we were able to categorize the label to different design attributes and compare and contrast the labels from two cafés. The main differences shown in the results from the labels are that Greem Café had Color and Cartoon attribute labels like “Black-and-White” and “Drawing” that clearly represent the cartoonish interior design style. On the other hand, Perception Coffee’s most frequent label, “Wood” along with other labels like “Aeolian Landform” identify their wooden ceiling design. With these results, it is shown that the Google Cloud Vision API is able to distinguish the main design elements from both cafés. This research utilizes a new tool that can be useful for future researchers and designers that deal with big data, also this research brings insight for designers at the time of creating new places by analyzing data made by consumers and promotes the usage of social media data as a tool for design thinking.