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October 3, 2025Turkish Journal of EngineeringOpen Access

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

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Authors

SSSatyendra SınghKKKrishan KumarBKBrajesh Kumar

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Overview

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

Key Points

  • 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.

Cite This Study

Sıngh et al. (2025) studied this question.

synapsesocial.com/papers/68e02f3cf0e39f13e7fa272fhttps://doi.org/10.31127/tuje.1698748
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Also Consider

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  5. 5Deep Learning for Multimodal Sentimental Analysis Using Long-Short Term Memory2025