BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910775835099136 |
|---|---|
| author | Mutsaddi, Atharva Jamkhande, Anvi Thakre, Aryan Haribhakta, Yashodhara |
| author_facet | Mutsaddi, Atharva Jamkhande, Anvi Thakre, Aryan Haribhakta, Yashodhara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03843 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study Mutsaddi, Atharva Jamkhande, Anvi Thakre, Aryan Haribhakta, Yashodhara Information Retrieval Computation and Language Machine Learning I.2.7; H.3.3; H.2.8 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. |
| title | BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study |
| topic | Information Retrieval Computation and Language Machine Learning I.2.7; H.3.3; H.2.8 |
| url | https://arxiv.org/abs/2501.03843 |