2
$\begingroup$

I'm planning on working on a project where I'll have a large collection of tweets about coronavirus vaccines. None of the tweets will have a label (e.g. positive, neutral, negative). Therefore I won't be able to train a model based on the labels.

I have a vague understanding of pre-trained models like BERT or VADER. I don't know however if I can use a model trained on other (text) data (like the ones mentioned above) and use it to run a sentiment analysis for the tweets I have.

Is it possible to do this? Or would it require labeled data in order to train the model with that specific data relating to the vaccine tweets?

$\endgroup$

1 Answer 1

2
$\begingroup$

You need at least a few labelled vaccine tweets (positive, neutral, negative) to train a BERT model so that it starts to understand the domain.

For VADER you don't need any labelled data.

However, when we compare the accuracies, the BERT model always performs better.

$\endgroup$
1
  • $\begingroup$ How about for example the Transformers API/HuggingFaces? $\endgroup$
    – LeLuc
    Commented Aug 21, 2021 at 18:21

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.