Evaluating Traditional And Transformer-Based Topic Models For Indian Financial News Analytics

Topic modeling allows the unsupervised learning of latent thematic representations in large textual collections which can in turn form the basis of semantic analyses and later uses such as trend analysis and classification. Considering the complexities of the Indian financial markets and the limitations of purely quantitative models, the study will conduct a comparative evaluation of the Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and BERTopic, on a dataset of 50,000 headlines and descriptions of Indian business news that were scraped from Business Standard between 2003 and May 2020. LDA is described as a probabilistic generative model, NMF as a non-negative ma-trix factorization model, and BERTopic as a transformer architecture that combines Sen-tenceBERT embedding with UMAP dimensionality reduction, HDBSCAN clustering, and class-based TF-IDF weighting. The qualitative analysis was conducted through the comparative analysis of salient words and word clouds. The alternative methods may not be as effective as BERTopic when evaluating contextual coherence and defining clear topical boundaries because it al-lows the discrimination between closely related financial subdomains. The findings empha-size the effectiveness of embedding-based topic modeling approaches in achieving greater interpretability and advancing the scope of financial text analytics.