I'm glad to have kept reading to the author's conclusion:
> As a hybrid approach, you could produce a large number of inferred sentiments for words, and have a human annotator patiently look through them, making a list of exceptions whose sentiment should be set to 0. The downside of this is that it’s extra work; the upside is that you take the time to actually see what your data is doing. And that’s something that I think should happen more often in machine learning anyway.
Couldn't agree more. Annotating ML data for quality control seems essential both for making it work, and building human trust.
> As a hybrid approach, you could produce a large number of inferred sentiments for words, and have a human annotator patiently look through them, making a list of exceptions whose sentiment should be set to 0. The downside of this is that it’s extra work; the upside is that you take the time to actually see what your data is doing. And that’s something that I think should happen more often in machine learning anyway.
Couldn't agree more. Annotating ML data for quality control seems essential both for making it work, and building human trust.