Constituency vs Dependency Parsing: What is more effective for Sentiment Analysis? Parsing is often used to understand the sentiment of complex sentences filled with double negations or very articulated.
There are two main ways of parsing a sentence: Constituency and Dependency Parsing. What is the most successful application for Sentiment Analysis?
Both approaches have been used for sentiment analysis in the literature. From a quick search, we can find these results:

Constituency parsing:

Deep Recursive Neural Networks for Compositionality in Language [NeurIPS'14]
Less Grammar, More Features[ACL'14]
A Statistical Parsing Framework for Sentiment Classification [Computational Linguistics journal'15]


Dependency parsing:

What's great and what's not: learning to classify the scope of negation for improved sentiment analysis [NeSp-NLP'10]
UBham: Lexical Resources and Dependency Parsing for Aspect-Based Sentiment Analysis [SemEval'14]



It seems to me that constituency parsing has been used more than dependency parsing. My sample, however, is very small and focused on the English language. I think this may change in languages with more complex (and more non-projective) syntax.
That been said, I think currently neither of them are considered to be state of the art in sentiment analysis. If we take a look at the leaderboards of sentiment analysis tasks from paperswithcode, all of the leading approaches (BERT, RoBERTa, M5) handle text as a mere sequence of tokens. While these results are from only a