2-label dataset for 3-label classifier?
Yes this is a good strategy. The only thing you need to figure out is the threshold value for negative to neutral, and neutral to positive.
What you could do is decide on some precision value (say 95%) that you want to achieve when classifying a positive article as positive and a negative article as negative. Based on this desired precision you can calculate the threshold you would need to set by evaluating your trained model on a holdout set (if the positive threshold is too low maybe you won't be able to acheive 95% precision, or if it's too high it might be too restrictive and label most headlines as neutral).
The design decision for these thresholds depends on the &quot;business problem&quot; you are trying to solve or the ideal client/use of your tool.