ADAPTIVE ONLINE REINFORCEMENT LEARNING FRAMEWORK FOR REAL-TIME OPINION MINING AND BIG DATA DECISION-MAKING IN SOCIAL MEDIA - Padwal, Nilam Deepak; Alaskar, Kamal (ShodhKosh: Journal of Visual and Performing Arts, 2024)
The tremendous surge of user-generated content in social media has naturally made these platforms important resources for public opinion, policy discussion, and market intelligence. Deriving actionable insights from such data requires models that are accurate and adaptive to fast-changing linguistic styles, misinformation campaigns, and current affairs. However, typical supervised learning models may not capture the variations over time and domains due to the nonstationary data distributions in social media. We address this problem and present a framework of Adaptive Online Reinforcement Learning (AORL) for online opinion mining and big data based decision making. The model combines deep sequential models for sentiment understanding with reinforcement learning agents which maintain an adaptive state over time. In particular, we consider three different architectures: (i) a bidirectional LSTM for contextual sentiment classification, (ii) a Deep Q-Network (DQN) for automated dialog policy learning, taking into account ensemble embeddings issued by the LSTM, and Vo} (iii) a RoBERTa-DQN hybrid which combines the power of transformer-based contextual embeddings with the flexibility of adaptive online learning. Experiments on large-scale Twitter streams show the referred AORL succeeds in achieving competitive classification accuracy yet it still remains robust against drifts and emerging trends. In addition, reinforcement signals are aligned with high-level decision-making goals, making applications possible in real-time crisis analysis, financial market news analysis and fake news control. This work represents a unique effort to operationalize online reinforcement learning in the big data social media domain, thereby paving the way toward scalable, adaptive, and credible opinion mining systems.