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.

本研究解决了印地语短文本主题建模方法不足的问题，探索了BERTopic在此领域的应用。通过使用上下文嵌入，BERTopic能够有效捕捉数据中的语义关系，并在捕获印地语短文本的连贯主题方面显著优于传统模型。研究结果表明，BERTopic在所评估的多种主题建模技术中表现最佳，具有重要的应用潜力。

BERTopic在印地语短文本主题建模中的比较研究