Topological Signature of 19th Century Novelists: Persistent Homology in Text Mining
AbstractTopological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textual document may reveal some additive information regarding the document that is not reflected in any other features from conventional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textual documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification. View Full-Text
Share & Cite This Article
Gholizadeh, S.; Seyeditabari, A.; Zadrozny, W. Topological Signature of 19th Century Novelists: Persistent Homology in Text Mining. Big Data Cogn. Comput. 2018, 2, 33.
Gholizadeh S, Seyeditabari A, Zadrozny W. Topological Signature of 19th Century Novelists: Persistent Homology in Text Mining. Big Data and Cognitive Computing. 2018; 2(4):33.Chicago/Turabian Style
Gholizadeh, Shafie; Seyeditabari, Armin; Zadrozny, Wlodek. 2018. "Topological Signature of 19th Century Novelists: Persistent Homology in Text Mining." Big Data Cogn. Comput. 2, no. 4: 33.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.