Adding Neutral Score 
# 🚀 Feature request

After performing some experimentation and comparison to VADER, we come to consensus that "pretrained BERT-based Hugging Face transfomer" is performing way beyond the other lexicons, but VADER is also good at social media context + it provides "neutral" label which turns out to be useful in some context. 

I was wondering whether it is possible to manipulate the Transformer Sentiment Analysis in a way that it can calculate the **"neutral" score**?


This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>Hey all, any chance anyone else is working around this? I think a neutral label or a standard sentiment score would be great for such an extensive model. Neutral statements are not caught with this adjustment:

classifier('I do not know the answer.')
Out[16]: [{'label': 'NEGATIVE', 'score': 0.9995205402374268}]

classifier('This is meant to be a very neutral statement.')
Out[17]: [{'label': 'NEGATIVE', 'score': 0.987031102180481}]

classifier('The last president of US is Donald Trump.')
Out[18]: [{'label': 'POSITIVE', 'score': 0.9963828325271606}]

classifier('There is going to be an election in two months.')
Out[19]: [{'label': 'NEGATIVE', 'score': 0.9604763984680176}]

Just raising this thread again to see if there is a common interest...
Cheers!