I am researching Sentiment Analysis over social media, particularly classifying online texts such as blog posts as positive, negative or neutral.
Most of the approaches I have found for sentiment analysis are supervised (they need labeled data to train a classifier). However, I have also found a couple of papers that do it using joint topic-sentiment models (unsupervised) like this one.
According to the results in the topic model papers, the main advantage of unsupervised approaches based on topic models is that they do no need any labeled data (apart from prior "general" sentiment information, i.e. a dictionary of positive/negative words). However, they do not reach the accuracy of a supervised approach (2% less of accuracy).
Are there any other advantages/disadvantages for using topic-sentiment models for sentiment classification instead of supervised approaches?