A Hybrid Based CNN-RNN Model for Improved Mining Techniques for Twetter Social Media Data and Analytics

Abstract:

This fast and quick growth of social media platforms have created vast repositories of unstructured data, necessitating advanced techniques to extract actionable insights. This study addresses the challenge of analyzing large-scale social media data by developing and evaluating deep learning models for sentiment classification. A preprocessing pipeline incorporating emoticon replacement, text normalization, tokenization, and stemming was applied to a dataset of 160,000 tweets. Two neural network architectures—a baseline Recurrent Neural Network (RNN) and a hybrid Convolutional Neural Network-Recurrent Neural Network (CNN-RNN)—were trained and compared. The hybrid CNN-RNN model demonstrated superior performance, achieving 76% accuracy, compared to the RNN’s 50%, underscoring the importance of combining local feature extraction with sequential dependency modeling. Temporal and lexical analyses further revealed trends in user engagement and sentiment expression. These findings highlight the effectiveness of hybrid deep learning architectures for social media analytics and provide a framework for future research in real-time sentiment monitoring.

• Periodical: Engineering Headway (Volume 37)
• Pages: 61-74
• DOI: https://doi.org/10.4028/p-xsNmk7
• Online since: March 2026
• Authors: Donatus O. Njioku*, Juliet N. Odii, Christopher I. Ofoegbu, Francisca O. Nwokoma, Uchenna C. Onyemauche, Innocent Harvey H. Ajunwa
• Keywords: Data Mining, Data Mining for Social Media, Data Mining Techniques, Social Media

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