A research paper proposes a hybrid framework for quantitative discourse analysis that integrates lexical and semantic methods. This approach aims to enhance transparency, reproducibility, and interpretability in computational discourse studies by combining custom Python pipelines with tools like NLTK, spaCy, Sentence Transformers, and BERTopic. The paper details an iterative BERTopic modeling process, including UMAP, HDBSCAN, and c-TF-IDF, to improve topic coherence and coverage, offering a multi-layered strategy to overcome the limitations of isolated methods. AI
IMPACT Enhances transparency and reproducibility in computational discourse analysis, potentially influencing how researchers utilize LLMs for text analysis.
RANK_REASON Research paper detailing a new methodology for discourse analysis. [lever_c_demoted from research: ic=1 ai=0.7]
- arXiv
- BERTopic
- GitHub
- hdbscan
- MAXQDA
- Natural Language Toolkit
- NVivo
- Python
- sentence_transformers
- SpaCy
- Uniform Manifold Approximation and Projection
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