arXiv cs.CL· Thomas Compton·· 11 小时前AI 评分8
被 arXiv 撤稿的 BERTopic 混合式定量话语分析框架论文
Beyond the Black Box: Integrating Lexical and Semantic Methods in Quantitative Discourse Analysis with BERTopic
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一篇提出结合词汇与语义方法的透明化定量话语分析（QDA）框架的论文已被 arXiv Admin 撤稿，理由是未达到 arXiv 的研究内容质量标准。该框架用 NLTK、spaCy 和 Sentence Transformers 构建自定义 Python 流程，并通过 UMAP 降维、HDBSCAN 聚类和 c-TF-IDF 关键词提取进行迭代式 BERTopic 建模。
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Abstract:Quantitative Discourse Analysis has seen growing adoption with the rise of Large Language Models and computational tools. However, reliance on black box software such as MAXQDA and NVivo risks undermining methodological transparency and alignment with research goals. This paper presents a hybrid, transparent framework for QDA that combines lexical and semantic methods to enable triangulation, reproducibility, and interpretability. Drawing from a case study in historical political discourse, we demonstrate how custom Python pipelines using NLTK, spaCy, and Sentence Transformers allow fine-grained control over preprocessing, lemmatisation, and embedding generation. We further detail our iterative BERTopic modelling process, incorporating UMAP dimensionality reduction, HDBSCAN clustering, and c-TF-IDF keyword extraction, optimised through parameter tuning and multiple runs to enhance topic coherence and coverage. By juxtaposing precise lexical searches with context-aware semantic clustering, we argue for a multi-layered approach that mitigates the limitations of either method in isolation. Our workflow underscores the importance of code-level transparency, researcher agency, and methodological triangulation in computational discourse studies. Code and supplementary materials are available via GitHub.
| Comments: | arXiv admin note: This submission has been withdrawn because it does not meet arXiv's research content quality standards |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2508.19099 [cs.CL] |
| (or arXiv:2508.19099v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2508.19099 arXiv-issued DOI via DataCite |
Submission history
From: arXiv Admin [view email]
[v1]
Tue, 26 Aug 2025 15:00:04 UTC (27 KB)
[v2]
Thu, 29 Oct 2026 19:48:19 UTC (1 KB) (withdrawn)
来源：arXiv cs.CL · arxiv.org