A scalable hybrid deep learning framework for mining actionable knowledge from large-scale and uncertain Twitter data - Abhilash Abhilash; Syed Siraj Ahmed (International Journal of Reconfigurable and Embedded Systems (IJRES), 2026)
Existing deep learning approaches often exhibit limitations in contextual comprehension, high computational overhead, and restricted generalization when processing large-scale, tweet-level, and semantically ambiguous text. Moreover, deploying such computationally intensive models in real-time internet of things (IoT)-enabled monitoring systems and embedded platforms introduces additional constraints related to latency, memory footprint, and energy efficiency. To address these challenges, this work proposes a scalable hybrid deep learning framework (SHDLF). The proposed framework effectively captures semantic, syntactic, and temporal dependencies in both short and long social media texts through a novel integration of transformer-based representations and attention-driven feature fusion mechanisms. The architecture is designed with a modular and parallelizable structure to facilitate hardware-aware optimization and potential deployment on embedded and reconfigurable computing platforms, enabling efficient edge-level processing of high-velocity Twitter streams. Extensive experimental evaluations conducted on a large benchmark Twitter dataset demonstrate that SHDLF consistently outperforms state-of-the-art models, including convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and baseline bidirectional encoder representations from transformers (BERT)-based architectures, in terms of accuracy, F1-score, and robustness under noisy conditions. The results confirm that SHDLF offers a robust, scalable, and computationally efficient solution for extracting reliable sentiment insights from noisy and dynamically evolving social media data.