Out of curiosity, have you done much work on examining your misclassifications? I'd be curious to know if there are giveaways for "negative" sentiment that show up in your task versus, say, reviews of Spiderman II.
In this work we didn't explore classification performance characteristics. I suspect the nature of the misclassification at lower levels of domain data would revolve around the ways language usage differs in reviews vs common english. "Blockbuster" may have generally negative or neutral sentiment in a wikipedia based language model, perhaps most often referring to the failed rental chain. In the context of movie reviews "blockbuster" is almost always universally positive.
