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.

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基于自然语言处理和机器学习技术的文本内引文分析十年：实证研究概述

引文分析是研究评价中最常用的方法之一。我们看到通过文献计量元数据进行的引文分析显着增长，这主要是由于引文数据库的可用性，例如 Web of Science、Scopus、Google Scholar、Microsoft Academic 和 Dimensions。由于近年来更好地访问全文出版物语料库，信息科学家已经远远超越了传统的文献计量学，通过利用全文数据处理技术的进步来衡量科学出版物在上下文方面的影响。这导致了引文上下文和内容分析、引文分类、引文情感分析、引文摘要和基于引文的推荐方面的技术发展。本文旨在叙述性地回顾有关这些发展的研究。它的主要重点是使用自然语言处理和机器学习技术来分析引文的出版物。