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Journal of Discrete Mathematical Sciences and Cryptography

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

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Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

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Journal of Discrete Mathematical Sciences and Cryptography
Open Access Research Article

A comparative analysis of conventional deep learning models with hybrid CNN-ResNet model for sentiment classification

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pp. 2595–2605Vol. 28Issue 6September 2025DOI: 10.47974/JDMSC-2463 Crossmark XML
Received:
10 Dec 2024
Published Online:
16 Sep 2025
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2463
Pages:
2595–2605

Abstract

The present work focuses on conventional deep learning models and introduces a proposed CNN–ResNet hybrid architecture for sentiment classification tasks. While traditional CNNs and RNNs have shown considerable success in NLP, they often struggle with capturing hierarchical semantic patterns. To address this, research integrates the deep feature extraction capability of ResNet into a CNN pipeline, improving contextual understanding and classification performance. Additionally, a diverse and labeled sentiment dataset and various domains, such as product reviews, social media, and general opinionated text has been used. The models were trained and evaluated on this dataset. Experimental results demonstrate that the proposed hybrid model achieves higher accuracy and robustness across multiple sentiment classes compared to baseline models. The work suggests that combining semantic richness with deep residual learning improves the reliability of sentiment classification systems.

Keywords

Sentiment classificationDeep learningCNNResNetSemantic featuresResidual learning

Subject Classifications

68T0768T5068T1068T45

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