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    Question

    Natural Language Processing (NLP) In the context of

    sentiment analysis, which of the following NLP techniques provides the most accurate classification of nuanced opinions?
    A Tokenization and Part-of-Speech (POS) tagging Correct Answer Incorrect Answer
    B Word2Vec embeddings Correct Answer Incorrect Answer
    C Transformer-based models like BERT Correct Answer Incorrect Answer
    D Latent Dirichlet Allocation (LDA) Correct Answer Incorrect Answer
    E N-gram Language Models Correct Answer Incorrect Answer

    Solution

    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:

    • A) Tokenization and POS tagging provide foundational text processing but lack the depth for nuanced opinion analysis.
    • B) Word2Vec embeddings capture word meanings but do not handle sentence-level context effectively.
    • D) LDA is primarily used for topic modeling and is not well-suited for sentiment classification.
    • E) N-gram models are limited by fixed context windows and fail to capture long-range dependencies in text.

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