r/MachineLearning - [D] Don't common sentiment analysis strategies seem unsatisfying?

There's lots of great projects in Reddit in sentiment analysis, but almost all of the work I've seen focuses on individual posts, as if tweets or reddit comments was simply a list of thumbs up and thumbs down about issues. For example, context, which doesn't seem to get much discussion. One very basic example where this is important: a Reddit comment that itself is booing a negative comment is considered negative. Of course, the nested "negative" comment should actually be counted in favor of the original topic. The relevant fields in NLP would be coreference, and possibly other subfields involving semantics.