Text mining Mill: Computationally detecting influence in the writings of John Stuart Mill from library records

Abstract

How can computational methods illuminate the relationship between a leading intellectual, and their lifetime library membership? We report here on an international collaboration that explored the interrelation between the reading record and the publications of the British philosopher and economist John Stuart Mill, focusing on his relationship with the London Library, an independent lending library of which Mill was a member for 32 years. Building on detailed archival research of the London Library’s lending and book donation records, a digital library of texts borrowed, and publications produced was assembled, which enabled natural language processing approaches to detect textual reuse and similarity, establishing the relationship between Mill and the Library. Text mining the books Mill borrowed and donated against his published outputs demonstrates that the collections of the London Library influenced his thought, transferred into his published oeuvre, and featured in his role as political commentator and public moralist. We reconceive archival library issue registers as data for triangulating against the growing body of digitized historical texts and the output of leading intellectual figures. We acknowledge, however, that this approach is dependent on the resources and permissions to transcribe extant library registers, and on access to previously digitized sources. Related copyright and privacy restrictions mean our approach is most likely to succeed for other leading eighteenth- and nineteenth-century figures.

Natural Language Processing and Fiction Text: Basis for Corpus Research

“…For example, NLP libraries can be used for text mining in the digital corpus of books [2]. Fonseca et al [3] apply NLP tools along with the linguistic corpora based on the XML tagging, analyzing the genre compositional structure.…”

“…see TIOBE Index for April 2023: https://www.tiobe.com/tiobe-index 2. spaCy Models & Languages: https://spacy.io/usage/models#languages.…”

Citation-augmented text reuse detection

“…They could then determine how useful this system is as a tool for their research. would also allow one to examine the utility of these methods in a cross-lingual setting, as he often cites sources in languages other than English that are also digitized (O'Neill et al, 2021). As the human record becomes more tractable to computation, models of source attribution promise not only to improve search but also to help us understand the reading and writing methods of the past.…”