A Comparison of Automatic Labelling Approaches for Sentiment Analysis. (arXiv:2211.02976v1 [cs.CL])

Labelling a large quantity of social media data for the task of supervised
machine learning is not only time-consuming but also difficult and expensive.
On the other hand, the accuracy of supervised machine learning models is
strongly related to the quality of the labelled data on which they train, and
automatic sentiment labelling techniques could reduce the time and cost of
human labelling. We have compared three automatic sentiment labelling
techniques: TextBlob, Vader, and Afinn to assign sentiments to tweets …

analysis arxiv comparison labelling sentiment sentiment analysis