Text Mining and Natural Language Processing in the Humanities: A Review of Methods and Applications in Historical Texts, Literature, and Social Media

Abstract

The integration of Text Mining and Natural Language Processing into the humanities has significantly
transformed the way scholars engage with textual data. As digital archives expand and textual information becomes increasingly available in digitized form, the need for computational methods to process, analyze, and interpret large volumes of unstructured data has grown. This paper presents a
comprehensive review of how text mining and NLP are being applied in the fields of historical research, literary analysis, and social media studies, with a focus on both their methodological foundations and practical implementations. The study begins with a literature review that traces the origins and evolution of text mining and NLP technologies, examining how they have been gradually adopted within the humanities. It then explores key tools and techniques such as tokenization, part-of-speech tagging, sentiment analysis, topic modelling, and named entity recognition. These methods have enabled researchers to extract valuable insights from massive textual corpora while maintaining academic rigour and interpretive depth. Through a series of case studies, the paper highlights applications in three major domains: historical texts, where patterns across centuries can be revealed; literature, where authorial styles and genre conventions are analyzed, and social media, where real-time discourse, identity construction, and public sentiment are studied. While the benefits of computational analysis are evident, the research also addresses limitations such as linguistic ambiguity, algorithmic bias, lack of historical linguistic resources, and ethical concerns surrounding data use and interpretation. By providing an interdisciplinary overview, this review contributes to the growing field of digital humanities and encourages a balanced, critical, and context-sensitive use of text mining and NLP techniques. The paper concludes with reflections on emerging trends, ethical frameworks, and future directions for research at the intersection of technology and humanistic inquiry.