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Implementation of Feature Extraction Using BERT in Aspect Based Sentiment Analysis

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BERT integration enhances aspect prediction accuracy up to 94%, improving overall performance in sentiment analysis.
The study shows that combining BERT with CNN and Bi-LSTM models significantly outperforms static embedding techniques.
Deep learning algorithms deployed include CNN, Bi-LSTM, and CNN+Bi-LSTM+Attention Mechanism to evaluate efficiency.
These findings indicate great potential for BERT in overcoming limitations of traditional static embeddings in sentiment analysis.

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Research demonstrates BERT boosts aspect prediction accuracy by 15% and sentiment accuracy by 11% in sentiment analysis tasks.

Turangan et al. (2025) studied this question.

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