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The research aims to develop a robust framework for sentiment analysis of Twitter data using a hybrid CNN-LSTM model.

June 19, 2026Scientific ReportsOpen Access

A hybrid CNN–LSTM approach for large-scale Twitter sentiment analysis with explainable artificial intelligence

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The research aims to develop a robust framework for sentiment analysis of Twitter data using a hybrid CNN-LSTM model.
Proposed a hybrid CNN-LSTM architecture for sentiment classification.
Evaluated the model on a balanced Twitter dataset of over 690,000 tweets.
Compared multiple deep learning models, including CNN, LSTM, Bi-LSTM, and the CNN-LSTM hybrid model.
The CNN-LSTM hybrid model achieved an accuracy of 97.73%, precision of 97.74%, and recall of 96.73%.
F1-score of the CNN-LSTM hybrid model was 97.73%, outperforming all baseline models.
Model interpretability using SHAP revealed sentiment predictions were based on relevant words.

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Randomized trial demonstrates improved sentiment classification in Twitter data, suggesting enhanced performance and interpretability.

Kılıç et al. (2026) studied this question.

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