Transformer-Enhanced Text Mining for Scalable Topic Modeling in Big Data Environments

Volume no :

6 |

Issue no :

1

Article Type :

Scholarly Article

Author :

A.Manoj Prabaharan

Published Date :

May, 2025

Publisher :

Journal of Science Technology and Research (JSTAR)

Page No: 1 - 12

Abstract : With the explosive growth of unstructured textual data in various domains such as social media, scientific literature, and enterprise records, scalable and accurate topic modeling has become crucial for extracting meaningful insights. Traditional topic modeling techniques like Latent Dirichlet Allocation (LDA) often struggle to handle the volume, velocity, and variety of big data while maintaining interpretability and computational efficiency. This paper proposes a novel approach that leverages transformer-based language models to enhance text mining and enable scalable topic modeling suitable for big data environments. Transformers, characterized by their self-attention mechanisms, have revolutionized natural language processing by capturing long-range dependencies and contextual semantics more effectively than previous methods. However, their direct application to large-scale topic modeling faces challenges due to computational demands and the complexity of adapting generative topic models to transformer architectures. To address these limitations, our approach integrates transformer embeddings with scalable clustering and dimensionality reduction techniques, creating a hybrid pipeline optimized for both performance and scalability. First, raw textual data undergoes preprocessing including tokenization, stop-word removal, and normalization. Subsequently, transformer models such as BERT or its variants generate high-dimensional contextualized embeddings representing semantic features of the documents. These embeddings encapsulate rich linguistic information, enabling the discovery of nuanced and coherent topics that traditional bag-of-words models may miss. To manage scalability, we employ approximate nearest neighbor search and hierarchical clustering algorithms, which reduce the computational burden when grouping documents into topic clusters. Dimensionality reduction techniques such as UMAP or PCA further streamline the embedding space, facilitating efficient topic extraction and visualization. This combination ensures the framework can process millions of documents without compromising topic quality or requiring extensive computational resources. We evaluate the proposed method on multiple large-scale benchmark datasets, including social media posts, scientific articles, and customer feedback collections. The results demonstrate significant improvements in topic coherence, relevance, and interpretability compared to baseline models including LDA and non-transformer embedding approaches. Moreover, our model exhibits robust scalability, with linear or near-linear time complexity growth relative to dataset size, making it well-suited for real-time and streaming data scenarios. In addition, the transformer-enhanced pipeline offers flexibility for downstream applications such as sentiment analysis, trend detection, and knowledge discovery, by providing semantically rich topic representations that facilitate deeper understanding and contextualization of big data content. The modular nature of the system allows easy integration with existing big data platforms and supports incremental updates to topic models as new data arrives. This work highlights the potential of combining state-of-the-art transformer architectures with scalable clustering methods to advance the field of topic modeling in big data contexts. Future research directions include optimizing transformer architectures for even greater efficiency, exploring multilingual and cross-domain topic models, and incorporating user feedback mechanisms to refine topic quality interactively. In summary, our transformer-enhanced text mining framework presents a scalable, accurate, and flexible solution for extracting meaningful topics from massive unstructured text corpora, empowering organizations to unlock actionable insights from big data environments.

Keyword: Transformer models, Text mining, Topic modeling, Big data, Scalability, BERT embeddings, Clustering, Dimensionality reduction, UMAP, Latent Dirichlet Allocation, Natural language processing, Semantic analysis, Large-scale text analysis, Document clustering, Computational efficiency

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Transformer-Enhanced Text Mining

Transformer-Enhanced Text Mining A primary obstacle in utilizing transformers directly for topic modeling is their computational
intensity and the difficulty in interpreting the resulting embeddings in terms of explicit topics.
Additionally, big data environments introduce additional constraints such as the need for real-
time processing, distributed computing, and incremental learning capabilities. To overcome
these hurdles, there is a growing interest in hybrid approaches that combine transformer-based
embeddings with scalable clustering and dimensionality reduction techniques, thereby
harnessing the semantic power of transformers while maintaining the efficiency required for big
data applications.

This paper proposes a novel transformer-enhanced text mining framework designed specifically
for scalable topic modeling in big data environments. Our approach involves generating
contextual embeddings from large corpora using transformer models, followed by scalable
clustering methods to group semantically similar documents into coherent topics. Dimensionality
reduction techniques further optimize the embedding space, enabling efficient computation and
visualization. This integrated pipeline not only improves topic quality but also addresses the
scalability challenges posed by massive datasets Transformer-Enhanced Text Mining.
We systematically evaluate the performance of our framework on diverse large-scale datasets
drawn from social media, scientific publications, and customer feedback, benchmarking against
classical topic modeling methods and non-transformer embedding baselines. The results
illustrate that our method significantly enhances topic coherence, interpretability, and relevance
while maintaining computational efficiency. Furthermore, the approach is adaptable to real-time
and streaming data, supporting incremental updates that are crucial for dynamic big data
ecosystems.

This paper presents a novel framework that leverages transformer-based language models to
enhance text mining and enable scalable topic modeling in big data environments. By integrating
powerful contextual embeddings generated from transformer architectures such as BERT with
scalable clustering and dimensionality reduction techniques, the proposed system addresses the
limitations of traditional topic modeling methods in handling massive and complex textual
datasets.

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