YuJa’s Semantic Topic Analysis provides an automated, topical overview of subjects covered in a capture or uploaded video. The technology is able to quickly categorize the information in the video and creates a visual mindmap for the viewer.
Super-topics become searchable tags, making it simple for students to find relevant content to further their academic goals.
Topics and super-topics enable students or instructors to quickly review lecture content, without watching the lecture.
Semantic Topic Analysis provides students with increased ability to make connections between different facts and subjects.
The topics and super-topics provide a quick, one-look summary of lecture capture or video content.
Semantic Topic Analysis starts with YuJa’s best-of-breed auto-captioning. Based on this, semantic topic analysis then creates searchable meta-layers, topic maps, and searchable tags.
Semantic Topic Analysis automatically reviews auto-captions to create an additional searchable topic meta-layer.
Lecture capture content is organized into topics in the lecture and super-topics that the topics fit into or under.
Super-topics become searchable, identifiable tags within the Media Management.
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