Labelling a dataset for sentiment analysis, which model is the best?

I want to do some sentiment analysis on a large text dataset I scraped. From what I've learned so far, I know that I need to either manually label each text data (positive, negative, neutral) or use a pre-trained model like bert and textblob.

1. I want to know which model has best accuracy in sentiment labelling. Both bi-polar (positive, negative) and tri-polar (positive, neutral, negative) are ok for the analysis I want to do.

2. If I want to make my own model that labels sentiment of each text, do I have to manually put all of them by hand, or is there a way that I can label some of the text rows, and put them in training to find out each sentiment of the rest? If there is, I want to know the name that I can study further.

Thanks!