Self-supervised Sentiment Analysis

Self-supervised sentiment analysis combines large-scale unsupervised pre-training — through objectives such as masked language modeling or contrastive prediction — with fine-tuning on a small labeled sentiment corpus. The approach, popularized by BERT and its variants, dramatically reduces the need for hand-labeled data while achieving state-of-the-art accuracy on positive/negative/neutral opinion classification tasks.