A large part of today's meeting was about Labeled LDA. The model was compared to standard LDA and Supervised LDA (http://www.cs.princeton.edu/~blei/papers/BleiMcAuliffe2007.pdf). It was mentioned that Supervised LDA is theoretically very nice, but in practice often is hard to get it working. For Labeled LDA, It was noted that the inference for the labels for the test documents was not worked out well (they had simplified it to standard LDA inference).
We also discussed the paper for discovering dialogue acts in Twitter. Overall, it wasn't clear was the goal was, how they defined dialogue acts, and therefore the evaluation was not very satisfying. We weren't sure what evaluation method would fit better.
We also discussed the overall popularity of looking at Twitter data nowadays. One of the main advantages is that it's easy to get (their API seems to be pretty good). Also, it's language is less formal than for example the WSJ, so could be more interesting if you're interested in more natural language.