While the idea to model statistical correlations between the words and tweet labels using PMI or any other metric is rather intuitive, we believe there is a more effective way to exploit noisy labels for estimating the word-sentiment association scores.
(Tang et al., 2014) showed that learning sentiment-specific word embeddings and using them as features can boost the accuracy of existing sentiment classifiers.