A Comparative Evaluation of LDA, NMF, and BERTopic: Analyzing Perplexity and Coherence Metrics
Bibliographic record
Abstract
Topic modeling plays a critical role in uncovering hidden semantic patterns within large text collections.This study offers a comparative evaluation of three widely used topic modeling techniques-latent dirichlet allocation (LDA), non-negative matrix factorization (NMF), and BERTopic-applied to a dataset of 446 scholarly abstracts related to Semantic Web research.The experimental design included standardized preprocessing steps and topic optimization tailored to each model.Performance was measured using Perplexity and Coherence (C_v) metrics, calculated through Gensim and BERTopic evaluation pipelines to ensure methodological reliability and reproducibility.The results demonstrate that the three models vary significantly in terms of interpretability, semantic accuracy, and computational efficiency.While LDA remains a dependable probabilistic baseline, the transformer based BERTopic model achieved notably higher coherence scores and superior semantic representation.These findings highlight the strengths and limitations of traditional and modern topic modeling approaches and emphasize their value in enhancing information retrieval, text classification, and automated knowledge discovery across academic and industrial contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".