I'm performing different sentiment analysis techniques for a set of Twitter data I have acquired. They are lexicon based (Vader Sentiment and SentiWordNet) and as such require no pre-labeled data. 
I was wondering if there was a method (like F-Score, ROC/AUC) to calculate the accuracy of the classifier. Most of the methods I know require a target to compare the result to.
What I did for my research is take a small random sample of those tweets and manually label them as either positive or negative. You can then calculate the normalized scores using VADER or SentiWordNet and compute the confusion matrix for each which will give you your F-score etc.
Although this may not be a particularly good test, as it depends on the sample of tweets you use. For example you may find that SentiWordNet classes more things as negative than VADER and thus appears to have the higher accuracy if your random sample are mostly negative.
Accuracy of lexicon-based sentiment analysis