Naive Bayes versus BERT in Sentiment Analysis

ChatGPT, OpenAI

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Naive Bayes in Sentiment Analysis:

Pros:

• Simplicity: Easy to implement and interpret.
• Efficiency: Works well with smaller datasets and requires less computational power.
• Baseline: Provides a strong baseline for comparison with more complex models.

Cons:

• Assumption of Independence: Assumes features (words) are independent, which is often not true in language processing.
• Limited Understanding: Cannot capture complex patterns or context, leading to lower accuracy with nuanced or complex text.

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BERT in Sentiment Analysis:

Pros:

• Contextual Understanding: Captures context and relationships between words, leading to higher accuracy.
• Transfer Learning: Pre-trained on large datasets, making it highly effective for various NLP tasks.
• Fine-Tuning: Can be fine-tuned for specific tasks, improving performance.

Cons:

• Computationally Intensive: Requires significant resources, especially for training and inference.
• Complexity: More difficult to implement and tune compared to simpler models like Naive Bayes.