In the example above, could social network analysis (e.g. https://en.wikipedia.org/wiki/NodeXL) be used to profile Sarah Palin, then combined with text classification?
It's an option, not sure about NodeXL, wikipedia is already available in structured form in Freebase and DBPedia, but understanding something so complex as a reputation is beyond current machine learning. Bringing the pragmatic background knowledge to sentiment analysis is going to be one of the differentiations. We got 65% easily with a lexicon, we got 75% with SVMs and gobs of training data, we've gone past that with hierarchical aspect models and technologies like word vectors, but the problem of making improvements gets exponentially harder. Before social networks can play a role in sentiment analysis likely we'll see breakthroughs in coreference and similar problems that will help eek out more signal from training data. We can certainly use the "hive mind" to assist in this problem, even something as simple as collaborative filtering can help.