[markovbling]

Reweighting sentiment by looking at the number of occurrences of positive and negative words in my assumed neutral corpus is a great idea :)

Will implement and report back

I've looked into using Naive Bayes but my understanding is you need labeled training documents and then I face the problem of scoring documents which introduces subjectivity compared to just counting the 'sentiment words'.

I understand complexity is needed to deal with negation ('not bad' != 'bad') but I'd imagine that the sentiment scoring process would be the same regardless of algorithm which brings us back to the problem of how to correct bias in 'word list' asymmetries