Discovering interesting usage patterns in text collections: integrating text mining with visualization

TitleDiscovering interesting usage patterns in text collections: integrating text mining with visualization
Publication TypeConference Papers
Year of Publication2007
AuthorsDon A, Zheleva E, Gregory M, Tarkan S, Auvil L, Clement T, Shneiderman B, Plaisant C
Conference NameProceedings of the sixteenth ACM conference on Conference on information and knowledge management
Date Published2007///
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-59593-803-9
Keywordsdigital humanities, frequent closed itemsets, n-grams, text mining, user interface
Abstract

This paper addresses the problem of making text mining results more comprehensible to humanities scholars, journalists, intelligence analysts, and other researchers, in order to support the analysis of text collections. Our system, FeatureLens1, visualizes a text collection at several levels of granularity and enables users to explore interesting text patterns. The current implementation focuses on frequent itemsets of n-grams, as they capture the repetition of exact or similar expressions in the collection. Users can find meaningful co-occurrences of text patterns by visualizing them within and across documents in the collection. This also permits users to identify the temporal evolution of usage such as increasing, decreasing or sudden appearance of text patterns. The interface could be used to explore other text features as well. Initial studies suggest that FeatureLens helped a literary scholar and 8 users generate new hypotheses and interesting insights using 2 text collections.

URLhttp://doi.acm.org/10.1145/1321440.1321473
DOI10.1145/1321440.1321473