Speaking in ML

Back to school Naaman? It has been a long summer. I had the pleasure of working with Jude Yew (you will enjoy the stylish cartoon drawing of himself) from the School of Information in Ann Arbor Michigan. We began the summer thinking about social networks and media sharing. We decided not to look at Twitter. Instead we looked back at Yahoo! Zync. We began to examine videos that were shared over IM in sync, how they were watched, and when people scrubbed. This became rather interesting and led us to ask questions about how we watch and consume and perceive videos.

To back up some, we started to look at videos just from YouTube. How they were classified. And how we could predict classification based on the video’s metadata. It turns out…its hard. We had a small dataset (under 2,000 videos) and getting a bigger crawl and throwing the data in the cloud was…well…just gonna take a little time. I get a little impatient.

We were using Naive Bayes to predict if a video was: Comedy, Music, Entertainment, Film, or News. The YouTube meta data had three features: the video length, the number of views, and the 5 star rating. We wondered about how people rate movies. Some B and even C movies are cult classics. They belong to a class of like media. It doesn’t say that a particular B movie isn’t as good as a particular A movie. If this is in fact the case, the set of 4.2 rated YouTube videos could be fit to a polynomial anywhere. In effect, they do not need to be before 4.5 and after 4.0. Technically put, the ratings of 0.0 to 5.0 could be transformed from interval to factors. With factorization, Naive Bayes has more freedom to fit polynomials to probabilistic distributions.

Only when we nominally factor the ratings can we classify videos on YouTube using only three features. Compared to random predictions with the YouTube data (21% accurate), we attained a mediocre 33% accuracy in predicting video genres using a conventional Naive Bayes approach. However, the accuracy significantly improves by nominal factoring of the data features. By factoring the ratings of the videos in the dataset, the classifier was able to accurately predict the genres of 75% of the videos.

The patterns of social activity found in the metadata are not just meaningful in their own right, but are indicative of the meaning of the shared video content. This was our first step this summer in investigating the potential meaning and significance of social metadata and its relation to the media experience. We’ll be presenting the paper Know Your Data: Understanding Implicit Usage versus Explicit Action in Video Content Classification (pdf) at IS&T/SPIE in January. Stop by and say hi if you see one of us there!

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