Advancing Message Board Topic Modeling Through Stack Ensemble Techniques

Abstract

In the digital era, message boards serve as vital hubs for diverse discussions, knowledge dissemination, and community interaction. However, navigating the vast and varied content on these platforms presents a formidable challenge. This research pioneers the utilization of stack ensemble techniques to revolutionize topic modeling on message board data. Integrating Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and Latent Semantic Analysis (LSA) within a sophisticated ensemble framework, this study introduces a paradigm shift in extracting nuanced insights. Incorporating domain-specific features, sentiment analysis, and temporal patterns enriches contextual understanding. Rigorous evaluation across diverse message board datasets underscores the ensemble method's unparalleled accuracy, stability, and interpretability, setting a new standard for discourse analysis in online communities. Keywords: Topic Modeling, Latent Dirichlet Allocation, Stack Ensemble Techniques, Natural Language Processing, Message Boards, Ensemble Learning