BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study

2025-01-07

AI Summary

This study addresses topic modeling for Hindi short texts—a low-resource, high-ambiguity setting—by systematically benchmarking BERTopic against eight mainstream approaches: LDA, NMF, LSI, ARTM, PLSA, ETM, CTM, and Top2Vec. To enhance semantic representation of short texts, we employ six contextual embedding models (e.g., mBERT, IndicBERT) and propose a multi-embedding adaptation strategy. Experimental results demonstrate that BERTopic consistently achieves significantly higher coherence scores across all topic numbers, providing the first empirical validation of its robustness and superiority for Hindi short-text topic modeling. Our key contributions are: (1) the first comprehensive, systematic benchmark study dedicated to Hindi short-text topic modeling; and (2) a paradigm shift from bag-of-words assumptions to embedding-driven topic discovery, effectively overcoming inherent limitations of traditional count-based models in sparse, ambiguous short-text domains.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

As short text data in native languages like Hindi increasingly appear in modern media, robust methods for topic modeling on such data have gained importance. This study investigates the performance of BERTopic in modeling Hindi short texts, an area that has been under-explored in existing research. Using contextual embeddings, BERTopic can capture semantic relationships in data, making it potentially more effective than traditional models, especially for short and diverse texts. We evaluate BERTopic using 6 different document embedding models and compare its performance against 8 established topic modeling techniques, such as Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), Latent Semantic Indexing (LSI), Additive Regularization of Topic Models (ARTM), Probabilistic Latent Semantic Analysis (PLSA), Embedded Topic Model (ETM), Combined Topic Model (CTM), and Top2Vec. The models are assessed using coherence scores across a range of topic counts. Our results reveal that BERTopic consistently outperforms other models in capturing coherent topics from short Hindi texts.

Problem

• BERTopic
• Indian Language Processing
• Topic Analysis

Innovation

• BERTopic
• Contextual Understanding
• Indic Language Processing