Linguistic data mining with complex networks: A stylometric-oriented approach
Authors:
- Tomasz Stanisz,
- Jarosław Kwapień,
- Stanisław Drożdż
Abstract
By representing a text by a set of words and their co-occurrences, one obtains a word-adjacency network being a reduced representation of a given language sample. In this paper, the possibility of using network representation to extract information about individual language styles of literary texts is studied. By determining selected quantitative characteristics of the networks and applying machine learning algorithms, it is possible to distinguish between texts of different authors. Within the studied set of texts, English and Polish, a properly rescaled weighted clustering coefficients and weighted degrees of only a few nodes in the word-adjacency networks are sufficient to obtain the authorship attribution accuracy over 90%. A correspondence between the text authorship and the word-adjacency network structure can therefore be found. The network representation allows to distinguish individual language styles by comparing the way the authors use particular words and punctuation marks. The presented approach can be viewed as a generalization of the authorship attribution methods based on simple lexical features. Additionally, other network parameters are studied, both local and global ones, for both the unweighted and weighted networks. Their potential to capture the writing style diversity is discussed; some differences between languages are observed.
- Record ID
- CUT9614817830cc4dfa8c5ea92b10407a67
- Publication categories
- ;
- Author
- Journal series
- Information Sciences, ISSN 0020-0255, e-ISSN 1872-6291
- Issue year
- 2019
- Vol
- 482
- Pages
- 301-320
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 319-320; Bibliografia (liczba pozycji) - 49; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 482
- Keywords in English
- complex networks, natural language, data mining, stylometry, authorship attribution
- DOI
- DOI:10.1016/j.ins.2019.01.040 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S002002551930043X Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 200
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT9614817830cc4dfa8c5ea92b10407a67/
- URN
urn:pkr-prod:CUT9614817830cc4dfa8c5ea92b10407a67
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.