What is the current state-of-the-art within aspect-based sentiment analysis?
Basically, there are two types of sentiment analysis.

Symbolic approach - the sentiment of the sentence is classified based on the kind of words used in the sentence. Involves a lexical database such as wordnet and some rules based on the grammatical structure of the language as knowledge representation.
Vector models - rather than detecting the sentiment in a sentence, the query sentence is classified into one of the category based on the training dataset and the features are represented as word vectors. Distributed and Distributional representations are types of it.

Whether it is coarse-classification (positive - negative) or fine-classification (types of emotions in it), a training dataset is needed if you are going to approach it via vector models as it is much more easier to play with.
The skip-gram model and continuous bag of words model developed in Google based on Recurrent neural network language model made a breakthrough in Natural language processing seems to be the state-of-art model in Distributional representation.
Since the code has been open-sourced, the c-code is available here and the python library is maintained here.
It doesn't matter if you are going to use reddit comments, tweets or reviews. What matters is how well the sentences are classified for the training data. Because garbage in is garbage out.