Recently, restaurant guide sites providing restaurant information posted by users on the Internet have been widely used as effective tools for consumers. Users, on a restaurant guide site, utilize IDs to post their recommendation articles on restaurants, and these posted articles are a valuable information source for other users. Open users can search for restaurants and read recommendation articles posted by other users. Furthermore, they can react (e.g., “like”) to a recommendation article when they feel it is helpful or they feel like visiting the restaurant. On a target restaurant guide site, each post includes the user ID, restaurant name, recommendation sentences, etc., and the number of reactions is considered to depend on these posted contents. For users who post recommendation articles, the number of reactions to their posts represents the degree of empathy from other users and is an important motivation for posting. Therefore, posting users will benefit from guidelines on how to write good recommendation sentences to increase the number of reactions. Moreover, the number of reactions can be regarded as an important indicator of the activity level of the restaurant guide site from the viewpoint of the service operating company. Therefore, an analytical model developed using historical information such as posts and reactions by users would be useful for determining the relationship between posted contents and the number of reactions. Therefore, this paper proposes a model based on the machine learning approach to analyze the relation between the number of reactions and posted contents. Finally, we demonstrate the analysis based on the proposed model using practical data.
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