I haven't looked in the specific classifications of this particular model, but what your comment shows is the importance (IMO) of having a "no sentiment" class when classifying sentiment. E.g. if someone says "John doe is an average guy", the sentiment to John is neutral. But if someone says "John doe is my uncle" there's no sentiment and it should be classified as that.
Perhaps the classifier here already takes this into account, but just thought it was worth mentioning the importance of having this extra class, or a separate pre-filter classifier.
In your example I also see many that could be filtered out. E.g. "I store them in Bitwarden not in dotfiles" doesn't contain negative/neutral/positive sentiment, or at least you're not able to tell from just this sentence.
I appreciate it's a fine line between neutral and no sentiment though.
There’s some old work [1] that conceptualized sentiment as an interplay between subjectivity and sentiment. The more subjective a statement, the more “range” sentiment gets. I think this is what you are getting at.
I don’t think it ever gained traction, probably because people aren’t interested in creating an actual theory of sentiment that matches the real world.
> E.g. "I store them in Bitwarden not in dotfiles" doesn't contain negative/neutral/positive sentiment, or at least you're not able to tell from just this sentence.
That's an interesting example because when I read it it sounds to me like something slightly positive, or at least, unlikely to be negative. Because if you had a negative opinion of Bitwarden, you probably wouldn't be storing stuff in it.
