Cross-Media Topic Modeling: A Comparative Analysis of NMF, LDA and CoRex combined with BERT on Quora and Twitter

This study undertakes a comprehensive analysis of topic modeling techniques-N on-negative Matrix Factorization (NMF), Latent Dirichlet Allocation (LDA), and Correlation Explanation (Corex) enhanced by BERT's depth on Quora and Twitter data. It evaluates their effectiveness in topic extraction and the impact of BERT's capabilities on topic categorization ac-curacy and interpretability. By applying NMF, LDA, and Corex, distinct topics were identified on both platforms, showcasing unique social media thematic structures. BERT's integration significantly improved contextual understanding, especially in sentiment detection and knowledge depth across topics. The findings highlight the strengths and limitations of traditional topic modeling methods for social media, and the benefits of incorporating BERT for deeper analysis. This research advances Natural Language Processing (NLP) by showing the value of hybrid approaches for extracting insights from complex social media content, suggesting directions for future research to enhance NLP methodologies' applicability and quality of being granular in social media analysis.