BBC Research and Development has developed a tool which allows viewers to use their mood to navigate TV programmes.
In a recent study by the Multimedia Classification project in Archives Research the majority of respondents said they would find it useful to be able to search for BBC programmes by mood.
In a BBC blog, Research Engineer Rosie Campbell explained how the system they're developing would work: "The classification system automatically analyses programmes for a range of different video and audio features, such as luminosity, laughter and motion. The results of this are then used to assign each programme a rating on a set of mood scales. For example, a programme with a high level of motion but not much laughter might score 5/5 on the 'slow-moving to fast-paced scale', but 1/5 on the 'serious to humorous' scale, meaning it is quick but not very funny (a thrilling drama for instance). The advantage here is that it is then possible to compare different programmes based on their mood scores, allowing you to, for example, search for something 'more exciting than Spooks'. Moreover, the combination of scores on each scale gives the programme a kind of mood fingerprint, so the system could recommend programmes with similar mood fingerprints to ones it knows you like."
The BBC team are currently in the process of researching how users might interact with this mood data and have created a prototype interface with a 2-dimensional scatter chart, with each axis representing one mood scale. According to the blog, this allows users to view the relative moods of programmes as dots plotted on the chart. They can then either pick the mood they want using the sliders, or search using conventional means, using mood as a reference.
The team intends the frontend to be integrated with BBC Redux (a BBC internal research tool) within a few months, as a preliminary trial to see how useful people find the interface.
Ultimately, the Multimedia Classification team hope to augment browsing, searching, skipping and programme recommendations utilizing mood-based metadata to the interface.
Image: Prototype interface
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