A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

In-text citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques to measure the impact of scientific publications in contextual terms. This has led to technical developments in citation classifications, citation sentiment analysis, citation summarisation, and citation-based recommendation. This article aims to narratively review the studies on these developments. Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations.

Automated summary

This article discusses the importance of in-text citation analysis in research evaluation and how advancements in full-text data processing techniques have been used to measure the impact of scientific publications. The focus of the research is on publications that have used natural language processing and machine learning techniques to analyze citations.