Discovering Interesting Usage Patterns in Text Collections: Integrating Text Mining with Visualization
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Discovering Interesting Usage Patterns in Text Collections: Integrating Text Mining with Visualization Anthony Don1, Elena Zheleva2, Machon Gregory2, Sureyya Tarkan2, Loretta Auvil4, Tanya Clement3, Ben Shneiderman1, 2 and Catherine Plaisant1 1Human Computer Interaction Lab 4National Center for Supercomputing Applications, 2Computer Science Department University of Illinois, USA 3English Department University of Maryland, USA {don,elena,mbg,sureyya,ben,plaisant} @cs.umd.edu, [email protected], [email protected] ABSTRACT online for their research. Those archives provide the raw material but researchers still need to rely on their notes, files and their own This paper addresses the problem of making text mining results memories to find “interesting” facts that will support or contradict more comprehensible to humanities scholars, journalists, existing hypotheses. In the fields of the Humanities, computers are intelligence analysts, and other researchers, in order to support essentially used to access to text documents but rarely to support the analysis of text collections. Our system, FeatureLens1, their interpretation and the development of new hypotheses. visualizes a text collection at several levels of granularity and enables users to explore interesting text patterns. The current Some recent works [4, 11] addressed this problem. One approach, implementation focuses on frequent itemsets of n-grams, as they supports the analysis of large bodies of texts by interaction capture the repetition of exact or similar expressions in the techniques together with a meaningful visualization of the text collection. Users can find meaningful co-occurrences of text annotations. For example Compus [4] supports the process of patterns by visualizing them within and across documents in the finding patterns and exceptions in a corpus of historical document collection. This also permits users to identify the temporal by visualizing the XML tag annotations. The system supports evolution of usage such as increasing, decreasing or sudden filtering with dynamic queries on the attributes and analysis using appearance of text patterns. The interface could be used to XSLT transformations of the documents. Another approach is to use explore other text features as well. Initial studies suggest that data-mining or machine learning algorithms integrated with visual FeatureLens helped a literary scholar and 8 users generate new interfaces so that non-specialists can derive benefit from these hypotheses and interesting insights using 2 text collections. algorithms. This approach has been successfully applied in the literature domain in one of our prior project [11]. Literary scholars Categories and Subject Descriptors could use a Naive Bayesian classifier to determine which letters of H.5.2 Graphical user interfaces (GUI), H.2.8 Data mining. Emily Dickinson's correspondence contained erotic content. It gave users some insights into the vocabulary used in the letters. General Terms: Algorithms, design, experimentation, While the ability to search for keywords or phrases in a collection is human factors, measurement. now widespread such search only marginally supports discovery because the user has to decide on the words to look for. On the Keywords: Text mining, user interface, frequent closed other hand, text mining results can suggest “interesting” patterns to itemsets, n-grams, digital humanities. look at, and the user can then accept or reject these patterns as interesting. Unfortunately text mining algorithms typically return 1. INTRODUCTION large number of patterns which are difficult to interpret out of Critical interpretation of literary works is difficult. With the context. This paper describes FeatureLens, a system designed to fill development of digital libraries, researchers can easily search and a gap by allowing users to interpret the results of the text mining retrieve large bodies of texts, images and multimedia materials thru visual exploration of the patterns in the text. Interactivity __________________________ facilitates the sorting out of unimportant information and speeds up 1 the task of analysis of large body of text which would otherwise be A video and an online demonstration are available from http://www.cs.umd.edu/hcil/textvis/featurelens/ overwhelming or even impossible [13]. FeatureLens aims at integrating a set of text mining and visualization functionalities into a powerful tool, which provokes Permission to make digital or hard copies of all or part of this work for new insights and discoveries. It supports discovery by combining personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that the following tasks: getting an overview of the whole text copies bear this notice and the full citation on the first page. To copy collection, sorting frequent patterns by frequency or length, otherwise, or republish, to post on servers or to redistribute to lists, searching for multi-word patterns with gaps, comparing and requires prior specific permission and/or a fee. contrasting the characteristics of different text patterns, showing CIKM’07, November 6-8, 2007, Lisboa, Portugal. Copyright 2007 ACM 978-1-59593-803-9/07/0011...$5.00. 213 patterns in the context of the text where they appear, seeing their computer programs [3, 7]. Instead of ranking documents according distributions in different levels of granularity, i.e. across to their content, FeatureLens ranks text patterns according to their collections or documents. Available text mining tools show the length and frequency, and it provides a visualization of the text repetitions of single words within a text, but they miss the support collection at the document level and at the paragraph level. These for one or more of the aforementioned tasks, which limits their two levels of granularity allow the user to identify meaningful usefulness and efficiency. trends in the usage of text patterns across the collection. It also We start by describing the literary analysis problem that enables the analysis of the different contexts in which the patterns motivated our work and review the related work. We then occur. describe the interface, the text mining algorithms, and the overall A recent interactive NY Times display [8] shows the natural system architecture. Finally we present several examples of use representation of the text of the State of the Union Addresses with with 3 collections and discuss the results of our pilot user studies. line, paragraph, and year categorization. It displays word frequency, location, and distribution information in a very simple manner 2. MOTIVATION which seemed to be readily understandable by the literary scholars This work started with a literary problem brought by a doctoral we have been interacting with. It allows search but does not suggest student from the English department at the University of words or patterns that might be interesting to explore. It also does Maryland. Her work deals with the study of The Making of not support Boolean queries. Americans by Gertrude Stein. The book consists of 517,207 Visualizing patterns in text is also related to visualizing repetitions words, but only 5,329 unique words. In comparison, Moby Dick in sequences. A number of techniques such as arc diagrams, repeat consists of only 220,254 words but 14,512 of those words are graphs and dot plots have been developed and applied to biological unique. The author’s extensive use of repetitions (Figure 1) makes sequence analysis [2, 5, 6]. Compared to DNA, literary text has The Making of Americans one of the most difficult books to read different structural and semantic properties such as division into and interpret in modern literature. Literature scholars are documents, paragraphs, sentences, and parts of speech that one developing hypotheses on the purpose of these repetitions and could use to create a more meaningful visualization. Arc diagrams their interpretation. have been used to visualize musical works and text, and have advantages over dot plots [15], though it has not been shown how they can be adapted to large collections of text without creating clutter. TextArc [9] is a related project, which visualizes text by placing it sequentially in an arc and allowing a user to select words interactively and to see where in the text they appear. It does not support ranking of patterns and selecting longer sequences of words. Most of the tools describe above only handle small datasets and display the collection as a fixed level of granularity. 4. FEATURELENS Figure 2 shows the graphical user interface of FeatureLens. The State of the Union Addresses collection consists of eight documents, one for each of President Bush’s eight annual speeches (there were two in 2001 because of 9/11). The documents are represented in the Figure 1: Extract from The Making of Americans. Document Overview panel. Each rectangular area represents one Recent critics have attempted to aid interpretation by charting the speech and its header contains the title of the document, i.e. the year correspondence between structures of repetition and the novel’s of the speech in this case. Within the rectangular representation of discussion of identity and representation. Yet, the use of repetition the document, each colored line represents a paragraph in this in The Making of Americans is far more complicated than manual collection. When the document is very large each line may represent practices or traditional word-analysis could indicate. The text’s a unit of text longer than a paragraph so that the overview remains large size (almost 900 pages and 3183 paragraphs), its particular compact. FeatureLens computes the default unit of text to be such philosophical trajectory, and its complex patterns of repetition that the overview fits on the screen, and users can change that value make it a useful case study for analyzing the interplay between using a control panel. For simplicity we call that arbitrary small unit the development of text mining tools and the way scholars of text a paragraph in the rest of the paper. develop their hypotheses in interpreting literary texts in general.