A Hybrid Ensemble Framework for Interpretable Topic and Sentiment Analysis of Social Media Content

The rapid evolution of user-generated content on social media platforms has formed a need for robust and interpretable analytical frameworks capable of organizing large sizes of textual data. This study suggests a hybrid ensemble clustering framework for combined topic and sentiment analysis of social media content. The framework combines multiple balancing clustering techniques to capture diverse structural patterns in text data, while sentiment polarity scores are combined to enhance semantic discrimination. A consensus-based ensemble approach is employed to generate stable and explainable cluster assignments, reducing the sensitivity of individual algorithms to noise and parameter selection. Dimensionality reduction is applied to support visualization and qualitative clarification of the discovered clusters. Experimental evaluation on a large-scale real-world dataset validates that the proposed hybrid approach constantly outperforms individual clustering methods in terms of cluster cohesion, separation, and interpretability. The results indicate that the framework delivers an effective and scalable solution for examining analysis of social media text, supporting downstream tasks such as trend identification, content organization, and decision support.