Text Analysis of Chemistry Thesis and Dissertation Titles.

Abstract

Programmatic text analysis can be used to understand patterns and reveal trends in data that would otherwise be difficult or impossible to uncover with manual coding methods. This work uses programmatic text analysis, specifically term frequency counts, to study nearly 10,000 chemistry thesis and dissertation titles from 1911-2015. The thesis and dissertation titles were collected from nine major research universities across the southeastern United States. The libraries of all nine are members of the Association of Southeastern Research Libraries (ASERL). Text analysis scripts were written in both MATLAB and Mathematica and used to extract the most common words and phrases from the titles. Some of the most common terms appearing in chemistry thesis and dissertation titles included synthesis, spectra, reaction, application, mass spectra, and nuclear magnetic resonance. Word usage over time was studied and used to reveal general research trends in chemistry. All data, programming scripts, and instruction methods are provided openly to the community. This article will be of interest to researchers and librarians interested in text analysis and chemistry research trends. [ABSTRACT FROM AUTHOR]

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Papers that cite the same references — the relational fallback while embeddings are still backfilling.