Transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) are highly effective in sentiment analysis due to their ability to understand context and semantics in both directions of a sentence. Unlike traditional models, BERT processes the entire text bidirectionally, capturing subtle nuances, such as sarcasm, negations, or contextual modifiers, that significantly impact sentiment. For example, in a sentence like "The service was not bad," BERT accurately identifies the positive sentiment by considering the negation. Additionally, its pre-training on massive datasets and fine-tuning for specific tasks make it robust for domain-specific sentiment analysis, offering unparalleled accuracy compared to other NLP techniques. Why Other Options Are Incorrect:
Four letter-clusters have been given, out of which three are alike in some manner and one is different. Select the letter-cluster that is different.
Four letter-clusters have been given, out of which three are alike in some manner and one is different. Select the letter-cluster that is different.
Pick the odd one out:
Select the odd letters from the given alternatives?
Find odd one out of all the given options.
Select the odd letters from the given alternatives?
Select the letter cluster which does not belong to the same group from the given alternatives.
Find the one which does not belong to that group?
Find the odd one out.
Find the odd one out.