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    <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.</abstract>
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%0 Conference Proceedings
%T BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study
%A Mutsaddi, Atharva
%A Jamkhande, Anvi
%A Thakre, Aryan Shirish
%A Haribhakta, Yashodhara
%Y Weerasinghe, Ruvan
%Y Anuradha, Isuri
%Y Sumanathilaka, Deshan
%S Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi
%F mutsaddi-etal-2025-bertopic
%X 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.
%U https://aclanthology.org/2025.indonlp-1.3/
%P 22-32

Markdown (Informal)

[BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study](https://aclanthology.org/2025.indonlp-1.3/) (Mutsaddi et al., IndoNLP 2025)

• BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study (Mutsaddi et al., IndoNLP 2025)

ACL

• Atharva Mutsaddi, Anvi Jamkhande, Aryan Shirish Thakre, and Yashodhara Haribhakta. 2025. BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study. In Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, pages 22–32, Abu Dhabi. Association for Computational Linguistics.