August 26, 2025InformaticaOpen Access

BERT-based Consumer Sentiment Analysis for Personalized Marketing Strategies

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The proposed model achieves 92.5% accuracy in sentiment classification, outperforming traditional methods.
Key evaluation metrics include accuracy, precision, recall, and F1 score for comprehensive assessment.
Preprocessing involved tokenization, stop word removal, and noise filtering for data cleaning.
Findings highlight the importance of transformer-based models for improving personalized marketing applications.

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Implication

This analysis finds a 7.7 percentage point increase in user conversion rates, suggesting that deep learning offers effective personalized marketing solutions.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/68af7c997567bf4f94ff45eb https://doi.org/10.31449/inf.v49i28.8232

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Implication

This analysis finds a 7.7 percentage point increase in user conversion rates, suggesting that deep learning offers effective personalized marketing solutions.

BERT-based Consumer Sentiment Analysis for Personalized Marketing Strategies

Key Points

Discussion

Implication

Cite This Study

BERT-based Consumer Sentiment Analysis for Personalized Marketing Strategies

Key Points

Discussion

Implication

Cite This Study

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Also Consider