Evaluating the Impact of Preprocessing Technique on Topic Modelling `Performance

Publication Details

Publication Date

10/09/2026

Volume / Issue

Vol 1, Issue 4 (2026)

Article No.

011

Journal

British Journal of Contemporary Research

DOI

https://doi.org/10.67693/BJCR-DWCEHS45

Received

02 Aug 2026

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5

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0

Abstract

In natural language processing (NLP), topic modeling has come to be an effective technique for identifying and extracting hidden topics from big textual datasets. However, the quality and consistency of the generated topics are significantly luenced by the preprocessing techniques performed on the text data prior to model training. This study compares Latent Dirichlet Allocation (LDA) with Non-Negative Matrix infFactorization (NMF) to see how preprocessing techniques affect topic modeling performance. We show that preprocessing has a significant impact on topic coherence using an evaluation metric. Our findings indicate that NMF, when combined with TF-IDF transformation, achieves a higher coherence score (0.5750) than LDA (0.4345), underscoring the significance of preprocessing in enhancing topic quality and interpretability.

License

This article is published under the Creative Commons Attribution 4.0 International License . Free to read, share, and adapt with attribution.