A topic-independent sentiment model is commonly used to estimate sentiment in microblogs. But for movie and product reviews, domain adaptation has been shown to improve sentiment estimation performance. We investigated the utility of topic-dependent polarity estimation models for microblogs. We examined both a model trained on Twitter tweets containing a target keyword and a model trained on an enlarged set of tweets containing terms related to a topic. Comparing the performance of the topic-dependent models to a topic-independent model trained on a general sample of tweets, we noted that for some topics, topic-dependent models performed better. We then propose a method for predicting which topics are likely to have better sentiment estimation performance when a topic-dependent sentiment model is used.