  The use of transfer learning methods is largely responsible for the present
breakthrough in Natural Learning Processing (NLP) tasks across multiple
domains. In order to solve the problem of sentiment detection, we examined the
performance of four different types of well-known state-of-the-art transformer
models for text classification. Models such as Bidirectional Encoder
Representations from Transformers (BERT), Robustly Optimized BERT Pre-training
Approach (RoBERTa), a distilled version of BERT (DistilBERT), and a large
bidirectional neural network architecture (XLNet) were proposed. The
performance of the four models that were used to detect disaster in the text
was compared. All the models performed well enough, indicating that
transformer-based models are suitable for the detection of disaster in text.
The RoBERTa transformer model performs best on the test dataset with a score of
82.6% and is highly recommended for quality predictions. Furthermore, we
discovered that the learning algorithms' performance was influenced by the
pre-processing techniques, the nature of words in the vocabulary, unbalanced
labeling, and the model parameters.
