A Review of Deep Learning and Transformer-based Architectures for Social Media Sentiment Analysis
The fast development of social media has contributed to the explosion of user-generated textual content that brings about substantial sentiment analysis possibilities on a massive scale. The sentiment classification methodologies have shifted in the last ten years in terms of traditional machine learning methods to deep learning architectures and even more recent to transformer-based models. This article describes a systematic review and analytical analysis of the techniques in sentiment analysis with emphasis on deep neural and transformer-based platforms. Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, as examples of classical architectures, are discussed with respect to their structural mechanism and contextual modeling capabilities and advanced transformer models including BERT and its variants are discussed with respect to their capability to capture the complex and context-specific sentiment patterns. This study is a synthesis of reported benchmark performance on popular datasets, as opposed to new experimental simulations, to establish trends in performance, which show an increasing performance improvement between the early state of deep learning models and the current state-of-the-art transformer based models. Moreover, the paper gives a comparative analysis of the efficiency-performance trade-offs, in the aspects of computational complexity, scalability limitations, and deployment restrictions in real-time settings. Gap analysis can further be structured to detect intractable research issues such as cross domain generalization, multilingual processing, implicit sentiment detection, interpretability, and bias mitigation. This review will not only provide contextual and theoretical knowledge about sentiment analysis but also practical development tips through integrating architectural analysis, benchmark trend synthesis, and research gap identification to build robust, scalable and context-aware sentiment analysis systems in the fast changing data ecosystem.