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Deep Hybrid CNN-LSTM Framework For Advanced Social Media Sentiment Analysis In Data-Driven Marketing Analytic

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The hybrid cnn-lstm model achieved an accuracy of 91%, showcasing its effectiveness in sentiment classification.
Comparative experiments showed that the cnn-lstm architecture outperformed traditional machine learning models like Naive Bayes and support vector machines.
The model was trained on various datasets including Twitter and Instagram, emphasizing the importance of preprocessing for accurate sentiment analysis.
The findings support the potential of hybrid models in enhancing real-time sentiment monitoring for customer experience improvement.

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This research demonstrates a hybrid cnn-lstm framework improving sentiment analysis in social media, suggesting potential in marketing analytics.

Mallick et al. (2025) studied this question.

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This research demonstrates a hybrid cnn-lstm framework improving sentiment analysis in social media, suggesting potential in marketing analytics.