How dependency parse tree is used for sentiment analysis?

Question

With the announcement from Google on release of Parsey McParseface syntaxnet 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.

Answer 1

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

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

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.

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.

Here's the paper I scanned:

http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS14/paper/viewFile/7869/7837

For a deeper exploration of whats possible, this might help:

https://arxiv.org/pdf/1401.6330.pdf

A more through introduction to dependency parsing if you're interested might be https://web.stanford.edu/~jurafsky/slp3/14.pdf

*somewhat in the sense that (in particular) convolution networks do learn a certain composition of language so do rnns.