Analysis of Dynamic Topic Modeling for Textual ‎Data Using A Novel Approach of Hybrid Deep Learning ‎with NMF and NTD

In this research, we conduct an in-depth analysis of dynamic topic modeling techniques for real-time and evolving textual data, leveraging methods such as Non-Negative Matrix Factorization ‎‎(NMF), Supervised NMF (SNMF), Non-Negative Tucker Decomposition (NTD), and hybrid ‎models, Hybrid HDP-CT-DTM, Hybrid DTM-RNN, and proposed Hybrid Convolutional Neural ‎Networks (CNNs) with NMF and NTD. Using the "Advanced Topic Modeling for Research ‎Articles 2.0" dataset, which comprises 14,000 documents, this research paper evaluates the ‎effectiveness of these methods based on perplexity, coherence, precision, recall, F-score, and ‎accuracy. Our findings indicate that the proposed hybrid CNN with NTD model outperforms ‎other techniques across all evaluation metrics, demonstrating superior ability in capturing complex ‎topic structures and maintaining high accuracy. This performance is attributed to the rich feature ‎extraction capabilities of CNNs and the higher-order interaction modeling provided by NTD. ‎This research work highlights the potential of advanced hybrid models for enhancing the quality ‎and interpretability of topic models in dynamic and large-scale textual datasets‎.