What are the art-of-state models in sentiment analysis?

There are several state-of-the-art models in sentiment analysis, some examples are:

  1. BERT (Bidirectional Encoder Representations from Transformers) and its variants like RoBERTa, ALBERT, and DistilBERT, which have shown to achieve state-of-the-art performance on a variety of sentiment analysis benchmarks.
  2. GPT-2 (Generative Pre-trained Transformer 2) and GPT-3 (Generative Pre-trained Transformer 3), which are pre-trained models based on the transformer architecture, have also been used in sentiment analysis tasks and have shown to perform well.
  3. XLNet, which is similar to BERT but uses a different pre-training approach called permutation-based training, has also been shown to perform well on sentiment analysis tasks.
  4. ULMFiT (Universal Language Model Fine-tuning), which is a transfer learning method for NLP tasks and has been used in sentiment analysis tasks and shown to perform well.
  5. T5 (Text-to-Text Transfer Transformer) is also a pre-trained model based on transformer architecture, which can be fine-tuned for various NLP task including sentiment analysis and has shown good performance.

These models are constantly evolving and improving, and new models and techniques are also emerging, so it’s worth keeping an eye on the latest developments in the field.