ADAPTIVE ONLINE REINFORCEMENT LEARNING FRAMEWORK FOR REAL-TIME OPINION MINING AND BIG DATA DECISION-MAKING IN SOCIAL MEDIA
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