Bibliometric analysis in Stata: Text mining, network community detection, and unsupervised learning

This presentation presents a bibliometric analysis workflow that combines Stata with its integrated Python environment to map the thematic structure of a large OpenAlex corpus and a dataset of abstracts in Scopus on the theme of energy policy. Relevant keywords are extracted from article titles through transparent linguistic preprocessing, domain-specific filtering, and frequency-based selection. The resulting article-by-keyword matrix is analyzed through principal component analysis to reduce dimensionality and identify the main latent thematic dimensions of the literature. K-means clustering is then applied to the retained component scores, assigning each article to a homogeneous research cluster. Stata manages data preparation, descriptive statistics, graphical outputs, and result export, while Python provides scalable text mining and machine learning procedures. The workflow produces an interpretable keyword dictionary, PCA loadings and scores, cluster memberships for individual articles, and cluster-level bibliometric profiles. A different approach based on the analysis of abstracts classifies them using cosine distance in an unsupervised framework. Finally, the co-occurrence matrix is analyzed using network analysis to identify the most relevant “cores” in the literature. The contribution is a practical workflow for conducting transparent, computationally efficient bibliometric analyses entirely within a Stata-centered research environment.