  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 context and content analysis, 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.
