Major reworking of SentimentAnalysis.py s.t. there is now a class for representing memes, contained in Meme.py, that contains methods for getting various feature representations out of it, etc. In addition, the only ngram classifier (in fact the only classifier at all) that we are using is a unigram-based maxent classifier that takes in features consisting of words that are contained in a meme with their location appended onto the end. that is, if wi occurs on the top, then two features you would see in its ngram feature rep would be {wi__TOP__:True, wi__ALL__:True}. empirically this seems to be classifying very well - having trained the classifier through only 5 iterations, such that it reached an accuracy of 0.93, it received an MRR of 0.76 and 0.77! this was with 0.85 of the data used for training, the remaining data used for testing. Next step is to make sentence classification work.