January 1, 2021

Customer Sentiment Analytics Using NLP and Deep Learning for Retail Insights

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Deep learning models significantly improve sentiment prediction accuracy compared to traditional methods.
Experiments showed that advanced techniques, like LSTM and CNN, capture context and sentiment polarity effectively.
The proposed framework includes data preprocessing and model training pipelines for analyzing customer-generated content.
Integration of sentiment scores aids in personalized marketing strategies, enhancing customer experience and loyalty.

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Analysis reveals improved sentiment classification using NLP and deep learning in retail, suggesting better customer insights.

Akonobi et al. (2021) studied this question.

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