Design an emotionally positive experience via sentiment classification for social media recommendation systems : A case study in TikTok

Abstract: Recommendation system benefits social media by attracting users with the posts they prefer. The recommended posts, however, may not align with what users really need to browse, especially in terms of emotion. Thus we conducted a case study in TikTok, in order to understand the emotional impact of social application’s post feed and to explore the interactive solution. The state-of-arts were reviewed, on the topics of psychology issues caused by social media, related therapy and product solutions. To empathise with users’ situation, a workshop was performed, consisting of a card game, presentation and participatory design. Then an emotion reminder, built on a Naive Bayesian text classifier and a facial expression SVM, was prototyped. With an accuracy of 0.51 (text) and 0.69 (facial expression) in sentiment classification, the emotion reminder was then tested by the users. It was discovered that users had higher emotion awareness, higher sense of control over the browsing and lower engagement in the interface with the prototype, compared with the original TikTok interface. And this was aligned with their needs described in the workshop. Users preferred the prototype’s content-based emotion detection than the detection based on their biological data in terms of privacy, and embraced the format of the reminder, instead of auto-filter, as an emotionally positive experience was not just browsing the posts with positive feelings, but receiving negative posts as well.