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A Comparative Analysis of Deep Learning Techniques for Sentiment Analysis Using Social Media Content

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This analysis compares deep learning models including LSTM and CNN for sentiment analysis, indicating the significance of pre-processing techniques.

The LSTM network achieved a high accuracy rate of 94.67% for sentiment analysis tasks.
1D-CNN and MLP follow LSTM in performance, showcasing deep learning superiority over traditional methods.
Various pre-processing techniques significantly enhance the performance of deep learning models in sentiment analysis.
Binary and ternary class sentiment analyses on public datasets reveal differing model efficacies.

Sıngh et al. (2025) studied this question.

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View Full Paper

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Authors

This analysis compares deep learning models including LSTM and CNN for sentiment analysis, indicating the significance of pre-processing techniques.

The LSTM network achieved a high accuracy rate of 94.67% for sentiment analysis tasks.
1D-CNN and MLP follow LSTM in performance, showcasing deep learning superiority over traditional methods.
Various pre-processing techniques significantly enhance the performance of deep learning models in sentiment analysis.
Binary and ternary class sentiment analyses on public datasets reveal differing model efficacies.

Sıngh et al. (2025) studied this question.

View Full Paper

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