Txt2vz: a new tool for generating graph clouds

We present txt2vz (txt2vz.appspot.com), a new tool for automatically generating a visual summary of unstructured text data found in documents or web sites. The main purpose of the tool is to give the user information about the text so that they can quickly get a good idea about the topics covered. Txt2vz is able to identify important concepts from unstructured text data and to reveal relationships between those concepts. We discuss other approaches to generating diagrams from text and highlight the differences between tag clouds, word clouds, tree clouds and graph clouds

Looking at a digital research data archive - Visual interfaces to EASY

In this paper we explore visually the structure of the collection of a digital research data archive in terms of metadata for deposited datasets. We look into the distribution of datasets over different scientific fields; the role of main depositors (persons and institutions) in different fields, and main access choices for the deposited datasets. We argue that visual analytics of metadata of collections can be used in multiple ways: to inform the archive about structure and growth of its collection; to foster collections strategies; and to check metadata consistency. We combine visual analytics and visual enhanced browsing introducing a set of web-based, interactive visual interfaces to the archive's collection. We discuss how text based search combined with visual enhanced browsing enhances data access, navigation, and reuse.Comment: Submitted to the TPDL 201

Scalable Programming for the Analysis of Aphasia Transcripts

Technologies designed for individuals with nonfluent aphasia focus on digitizing speech therapy methods and generating speech. To improve these technologies, the language characteristics of individuals with non- fluent aphasia must be further understood. Language corpuses, such as the AphasiaBank, provide a promising solution for informing technology usability in terms of navigation, interface, and content decisions. As a tool for informing such work, this research investigates the viability of a flexible and scalable multi-threaded software program for the analysis of AphasiaBank transcripts. Results show that the program allows rapid analysis of all transcriptions by optimizing core functionality and minimizing the number of areas for synchronization. This research aims to improve the access to information, products, and services in technology for individuals with non-fluent aphasia

A Picture is Worth 150 Words: Using Wordle to Assess Library Instruction

Tired of the one minute paper and other quick and dirty assessment tools? By using word clouds, students can demonstrate their grasp of library fundamentals and information literacy concepts in less than 10 minutes. Wordle [http://www.wordle.net] is an extremely user-friendly online tool that provides an active learning activity for students and allows librarians to rapidly evaluate what students recall from the instruction session. Use it for quick assessment of student comprehension of library jargon or compare the students\u27 Wordle clouds with information literacy standards or the main points of your instruction. It\u27s free, flexible, and looks great on a t-shirt

On Semantic Word Cloud Representation

We study the problem of computing semantic-preserving word clouds in which semantically related words are close to each other. While several heuristic approaches have been described in the literature, we formalize the underlying geometric algorithm problem: Word Rectangle Adjacency Contact (WRAC). In this model each word is associated with rectangle with fixed dimensions, and the goal is to represent semantically related words by ensuring that the two corresponding rectangles touch. We design and analyze efficient polynomial-time algorithms for some variants of the WRAC problem, show that several general variants are NP-hard, and describe a number of approximation algorithms. Finally, we experimentally demonstrate that our theoretically-sound algorithms outperform the early heuristics

Customer Feedback Analysis using Collocations

Today’s ERP and CRM systems provide companies with nearly unlimited possibilities for collecting data concerning theircustomers. More and more of these data are more or less unstructured textual data. A good example of this type of data iscustomer feedback, which can potentially be used to improve customer satisfaction.However, merely getting an overview of what lies in an unstructured mass of text is an extremely challenging task. This isthe topic of the field of computational linguistics. Collocation analysis, one of the tools emerging from this field, is a tooldeveloped for this task in particular. In this paper, we use the collocation analysis to study a text corpora consisting of 64,806pieces of customer feedback collected through a case company’s online customer portal. Collocation analysis is shown to bea very useful tool for exploratory analysis of highly unstructured customer feedback