  Neural network-based embeddings have been the mainstream approach for
creating a vector representation of the text to capture lexical and semantic
similarities and dissimilarities. In general, existing encoding methods dismiss
the punctuation as insignificant information; consequently, they are routinely
eliminated in the pre-processing phase as they are shown to improve task
performance. In this paper, we hypothesize that punctuation could play a
significant role in sentiment analysis and propose a novel representation model
to improve syntactic and contextual performance. We corroborate our findings by
conducting experiments on publicly available datasets and verify that our model
can identify the sentiments more accurately over other state-of-the-art
baseline methods.
