BERT opic for Topic Modeling of H indi Short Texts: A Comparative Study (2025)

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.

## introduction topic modeling is a widely-used technique in text mining that identifies underlying themes within textual data. bertopic, a newer model in this field, has demonstrated its effectiveness by using pre-trained document embedding models and unsupervised clustering algorithms to form topic groups with high semantic coherence @xcite . unlike traditional models, bertopic's use of embeddings allows it to capture contextual information, such as identifying named entities and associating them with relevant topic clusters that older approaches struggle with @xcite . existing research on topic modeling for hindi texts has largely focused on traditional methods that rely on probabilistic frameworks and matrix factorisation, which often overlook natural language semantics @xcite . also, these studies primarily focus on long text documents, leaving a gap in the exploration of short hindi texts, which are increasingly common in todays digital landscape. topic modeling in hindi faces several unique challenges. hindi does not use capitalisation to differentiate proper nouns from other word forms, complicating named entity recognition. additionally, the lack of standardised spelling leads to multiple variations of the same word (figure 1 ), creating ambiguity. hindi also often employs repetitive expressions for emphasis, which can affect tokenization and cross-language natural language processing tasks @xcite . this study aims to demonstrate that traditional topic models often fall short in capturing the semantic meaning of hindi text due to these inherent challenges and struggle with the nuances of short texts where semantic meaning is more compressed and context-dependent. the contributions of this paper are as follows: ## previous work topic modeling studies on hindi text have predominantly relied on traditional frameworks, such as probabilistic models and matrix factorisation techniques, while newer approaches remain largely under-explored. furthermore, most of these studies have focused on long text documents, leaving the effectiveness of topic modeling techniques on short texts inadequately examined. @xcite provided an overview of various topic modeling approaches for hindi text, including methods like nmf @xcite , lsi @xcite , and lda @xcite , as well as tools and java packages used in these models. however, their work predates the development of bertopic and does not address its application. similarly, srivastav and singh (2022) investigated the use of models such as lda, doc2vec, and hierarchical dirichlet process (hdp) @xcite for identifying the main topics in news articles in both hindi and english. their study, however, also did not consider newer topic modeling techniques like bertopic. panigrahi et al. ( 2018 ) explored an embedding-based clustering approach, using word2vec @xcite to generate word embeddings from a corpus of hindi wikipedia articles, which were subsequently clustered into topic groups. while this study adopted an approach similar to bertopic, it did not specifically focus on short texts or use more advanced document embedding models. while bertopic's effectiveness on hindi texts remains unexplored, some studies have evaluated its performance in other non-english languages. @xcite compared bertopic using different sentence transformers against lda and nmf on arabic news articles, and found that bertopic formed more coherent topic clusters by evaluating them using normalised pointwise mutual information @xcite . another study @xcite compared the computational cost and topic quality of lda, etm, ctm, nmf, and two bertopic variants on arabic data, concluding that bertopic outperformed other models in coherence scores. although these studies focused on longer texts, @xcite demonstrated that bertopic produced more informative clusters than lda and nmf when applied to serbian tweets, showing its efficiency in modeling short text data in other languages. although bertopic's superior performance has been proven for some other non-english languages, its effectiveness for hindi, especially on short texts, remains un-examined. given hindi's unique linguistic challenges @xcite and its status as the third most spoken language globally, it is important to evaluate bertopic's performance, particularly for hindi short texts, which are increasingly common in modern media. ## methodology this section gives an overview about the dataset we used, the topic models we evaluated and the method of evaluation used for this comparison. ## results and analysis we compared 20 models, each utilising different approaches, including embedding-based models like bertopic and etm with the 5 embedding models mentioned in subsection 3.3, hybrid models like ctm and top2vec using 4 pre-trained embedding models (subsection 3.4), probabilistic models like lda, artm, and plsa, and matrix factorisation models such as nmf and lsi. following are our findings: ## discussion this study demonstrates that topic models utilising advanced sentence transformers, such as bertopic and etm, significantly outperform traditional models when modeling short texts. the success of these models can be attributed to their ability to capture semantic meaning beyond simple word co-occurrence patterns. traditional topic modeling algorithms like plsa and lda are widely used to uncover latent semantic structures in text corpora by relying on word co-occurrence patterns at the doc-ument level. however, these methods require a high frequency of word co-occurrences to generate meaningful topics, leading to significant performance degradation when applied to short texts where such information is sparse @xcite . similarly, the performance of lsi declines over short texts as the detected topics become ambiguous, resulting in negative values in its decomposed matrices that are difficult to interpret @xcite . since many of these traditional models depend heavily on word frequency and cooccurrence, they are more sensitive to variations in spelling, a common issue in hindi due to the lack of standardised spelling conventions @xcite . these limitations collectively undermine the reliability of traditional models in generating coherent topics from short text corpora. ## conclusion we evaluated the performance of bertopic relative to other topic models using coherence values (c v ) and normalised pointwise mutual information (c npmi ) across a range of 5 to 210 topics. the results showed that bertopic, particularly when used with mbert-uncased, outperformed other models for the majority of topic counts. the etm model, using hindsbert-sts, ranked second, with better c npmi scores than bertopic beyond 125 topics, but consistently lower c v scores. traditional topic models demonstrated poor performance, having negative c npmi scores for the entire topic count range. qualitative analysis of bertopic clusters revealed that it effectively grouped semantically similar reviews and accurately recognised named entities, a task at which traditional models struggle. the strong performance of both etm and bertopic suggests that leveraging advanced sentence transformers enhances the formation of coherent topic clusters. we conclude that bertopic is a promising approach for topic modeling on hindi short text corpora, particularly when using multilingual transformers fine-tuned on hindi. its use can produce semantically coherent topic groups and better handle the unique linguistic complexities of the language. potential applications include trend analysis, extracting business insights, analysing customer reviews and social media comments.