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We study the problem of online multitask learning for solving multiple related classification tasks in parallel, aiming at classifying every sequence of data received by each task accurately and efficiently. One practical example of online multitask learning is the micro-blog sentiment detection on a group of users, which classifies micro-blog posts generated by each user into emotional or non-emotional...
We study the online micro-blog sentiment detection problem, which aims to determine whether a micro-blog post expresses emotions. This problem is challenging because a micro-blog post is very short and individuals have distinct ways of expressing emotions. A single classification model trained on the entire corpus may fail to capture characteristics unique to each user. On the other hand, a personalized...
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