<p>Good question, Im not expert, in fact I got intrigued when you asked the question. </p>

<p><strong>td;lr;</strong> more accurate dependency parsers would in allow one propagate sentiment through a graph and thus better determine sentiment polarity, at least in theory.</p>

<p>It seems from my reading that sentiment analysis using dependency tree graphs propagate the independent (prior -- sentiment you might get from a lexicon) sentiment of words to compose overall sentiment polarity of the text. </p>

<p>This approach uses the composition of language (its grammatical structure) to determine sentiment. This is somewhat* opposed to a statistical (naives bayes, logistics regression, neural networks) approach to understanding sentiment.</p>

<p>Here's the paper I scanned:</p>

<p><a href="http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS14/paper/viewFile/7869/7837" rel="nofollow noreferrer">http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS14/paper/viewFile/7869/7837</a></p>

<p>For a deeper exploration of whats possible, this might help:</p>

<p><a href="https://arxiv.org/pdf/1401.6330.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1401.6330.pdf</a></p>

<p>A more through introduction to dependency parsing if you're interested might be <a href="https://web.stanford.edu/~jurafsky/slp3/14.pdf" rel="nofollow noreferrer">https://web.stanford.edu/~jurafsky/slp3/14.pdf</a> </p>

<p>*somewhat in the sense that (in particular) convolution networks do learn a certain composition of language so do rnns.</p>
How dependency parse tree is used for sentiment analysis?
<p>With the announcement from Google on release of Parsey McParseface <a href="https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html" rel="nofollow noreferrer">syntaxnet</a>
which is claimed to be the most accurate dependency parser. I want to understand how this parser can be used for more accurate sentiment analysis ? If someone can share some blogs or research papers or tutorials which can help me understand the overall flow.</p>