MLalgos

Couldn't sleep tonight, but made some progress on the algos:

Unsupervised - added an additional component to normalize the PMI sentiment orientation score. It is now MUCH more accurate at least according to the eyeball test. More thorough testing is difficult for now as google shuts down api access after a series of queries in short succession.

Supervised - rewrote large portions of the code, now optimized with pandas and numpy and runs significantly faster. I also realized that the data in our test samples are almost always biased one way or the other. It now manually parses the data to grab an even number of positive and negative cases. Unfortunately, as expected this lowers the accuracy to the low 70's. Will work on more thorough feature engineering tomorrow
is it possible to derive a sense of confidence from the SO? For instance, is there a best possible and worst possible SO that we could compare an actual SO aginst to determine confidence? Or maybe recycle the ratios above this line somehow? Not sure if the math is sound or not... it'd just be nice to have a sense of confidence from this function for comparison against the results of other algorithms so that we can cherry pick the highest confidence answer.