DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos

Abstract

Danmu (Danmaku) is a unique social media service in online videos, especially popular in Japan and China, for viewers to write comments while watching videos. The danmu comments are overlaid on the video screen and synchronized to the associated video time, indicating viewers' thoughts of the video clip. This paper introduces an interactive visualization system to analyze danmu comments and associated viewer behaviors in a collection of videos and enable detailed exploration of one video on demand. The watching behaviors of viewers are identified by comparing video time and post time of viewers' danmu. The system supports analyzing danmu content and viewers' behaviors against both video time and post time to gain insights into viewers' online participation and perceived experience. Our evaluations, including usage scenarios and user interviews, demonstrate the effectiveness and usability of our system.

Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method

“…First, TSCs are generally posted by viewers quickly, and thus the text length is much shorter than that of traditional comments, which means that the semantic information from a certain TSC is limited or even ambiguous (He et al, 2018). Second, TSCs usually contain popular phrases and Internet slang related to particular fields, the informal expression of TSCs makes semantic representation intractable (Chen et al, 2022). For example, Figure 1 gives one video comment and three TSCs from the same online video.…”