A review on authorship attribution in text mining
A review on authorship attribution in text mining
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DOI：
10.1002/wics.1584
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Wanwan Zheng;Mingzhe Jin
中科院分区：
文献类型：
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作者：
Wanwan Zheng;Mingzhe Jin
作者归属问题长期以来一直被认为是一个热门话题。由于数字计算机的进步，这一领域在过去十年中经历了迅速的发展。本文综述了文本挖掘中作者归属的最新研究进展。这项调查的重点是作者归属方法，统计或计算支持，而不是传统的文学方法。涵盖的主要方面包括研究主题随时间的变化，基本特征度量，机器学习技术以及每种方法的优点和缺点。此外，语料库的大小，候选人的数量，数据不平衡，和结果的描述，所有这些都对作者归属的挑战，进行了讨论，以告知未来的工作。
The issue of authorship attribution has long been considered and continues to be a popular topic. Because of advances in digital computers, this field has experienced rapid developments in the last decade. In this article, a survey of recent advances in authorship attribution in text mining is presented. This survey focuses on authorship attribution methods that are statistically or computationally supported as opposed to traditional literary approaches. The main aspects covered include the changes in research topics over time, basic feature metrics, machine learning techniques, and the advantages and disadvantages of each approach. Moreover, the corpus size, number of candidates, data imbalance, and result description, all of which pose challenges in authorship attribution, are discussed to inform future work.