Sentiment Analysis of Social Network Data Using Traditional and Hybrid Deep Learning Models
Received: 21 June 2025 | Revised: 21 July 2025 and 16 August 2025 | Accepted: 30 August 2025 | Online: 7 October 2025
Corresponding author: Abdimukhan Tolep
Abstract
Social networking platforms generate vast volumes of short, informal user-generated text that encode opinions and attitudes. Organizations seek to analyze this content to derive actionable insights, motivating robust sentiment-analysis methods. This study presents an empirical comparison of deep-learning approaches for sentiment classification of social-media text. We evaluate six standalone models—Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Recursive Neural Network (RecNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU)—and three hybrid variants (CNN–RNN, CNN–LSTM, CNN–BiLSTM). The corpus undergoes standard preprocessing (cleaning, tokenization, lemmatization, stop-word removal) before classification. Across the tested settings, the CNN–BiLSTM consistently achieves the strongest overall performance on key metrics (e.g., recall, F1-score, AUC-ROC), outperforming both standalone architectures and the other hybrids. These findings indicate that combining localized n-gram feature extraction with bidirectional sequence modeling yields a favorable balance of accuracy and robustness for short, noisy posts, and they offer practical guidance for selecting architectures for social-media sentiment analysis.
Keywords:
sentiment analysis, deep learning, hybrid models, classification, NLP, social mediaDownloads
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Copyright (c) 2025 Abdimukhan Tolep, Satmirza Mamikov, Zhansaya Yakhiya, Ainur Abdullayeva
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