How does this compare to other benchmarks? It looks like you're using the sentence-level dataset out of Cornell, based on your Github. Even a naive unigram baseline easily beats the 70% threshold you mentioned in your post. A few years ago, I co-authored [1] a publication with very similar graph-based features on this dataset that achieved 77% accuracy, and the state of the art has moved beyond that since then. Without a comparison to baseline it's hard to tell whether this (much more sophisticated) technique is adding value.

[1] Shilpa Arora, et al. "Sentiment classification using automatically extracted subgraph features." Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text.