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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:
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