\u27Harlem Exchange\u27 Relies on Letters Between Friends of Three Decades

News release announces the Harlem Exchange, an epistolary play, will be presented at the University of Dayton

Visual Analytics of Change in Natural Language

This thesis describes novel computer science research on visual analytics methods for the detection and understanding of diverse phenomena of change that can be observed either within natural language text or based on it. The term change refers to the observable variation of features and patterns over time. In particular, two different kinds of phenomena are under research. The first part of the thesis deals with the diachronic change of linguistic features, namely language change. It includes pioneering work in the intersection of the disciplines of historical linguistics typological comparison of languages and visual analytics and contributes to the broader field of digital humanities or enhanced humanities (eHumanities). The second part of the thesis deals with visual analytics methods for the interactive detection and exploration of sudden unexpected changes in the information content conveyed by a large-scale text data stream. The research fills gaps in the previous work on time-related visual text analytics, demonstrates the commercial potential of such methods, and systematically outlines future research challenges for the live analysis and visualization of large-scale text data streams

PhonMatrix: Visualizing co-occurrence constraints of sounds

christian.rohrdant

PhonMatrix: Visualizing co-occurrence constraints of sounds

Abstract This paper describes the online tool PhonMatrix, which analyzes a word list with respect to the co-occurrence of sounds in a specified context within a word. The cooccurrence counts from the user-specified context are statistically analyzed according to a number of association measures that can be selected by the user. The statistical values then serve as the input for a matrix visualization where rows and columns represent the relevant sounds under investigation and the matrix cells indicate whether the respective ordered pair of sounds occurs more or less frequently than expected. The usefulness of the tool is demonstrated with three case studies that deal with vowel harmony and similar place avoidance patterns

Analyzing Document Collections via Context-Aware Term Extraction

In large collections of documents that are divided into predefined classes, the differences and similarities of those classes are of special interest. This paper presents an approach that is able to automatically extract terms from such document collections which describe what topics discriminate a single class from the others (discriminating terms) and which topics discriminate a subset of the classes against the remaining ones (overlap terms). The importance for real world applications and the effectiveness of our approach are demonstrated by two out of practice examples. In a first application our predefined classes correspond to different scientific conferences. By extracting terms from collections of papers published on these conferences, we determine automatically the topical differences and similarities of the conferences. In our second application task we extract terms out of a collection of product reviews which show what features reviewers commented on. We get these terms by discriminating the product review class against a suitable counter-balance class. Finally, our method is evaluated comparing it to alternative approaches

“Beautiful picture of an ugly place” : Exploring photo collections using opinion and sentiment analysis of user comments

User generated content in the form of customer reviews, feedbacks and comments plays an important role in all types of Internet services and activities like news, shopping, forums and blogs. Therefore, the analysis of user opinions is potentially beneficial for the understanding of user attitudes or the improvement of various Internet services. In this paper, we propose a practical unsupervised approach to improve user experience when exploring photo collections by using opinions and sentiments expressed in user comments on the uploaded photos. While most existing techniques concentrate on binary (negative or positive) opinion orientation, we use a real-valued scale for modeling opinion and sentiment strengths. We extract two types of sentiments: opinions that relate to the photo quality and general sentiments targeted towards objects depicted on the photo. Our approach combines linguistic features for part of speech tagging, traditional statistical methods for modeling word importance in the photo comment corpora (in a realvalued scale), and a predefined sentiment lexicon for detecting negative and positive opinion orientation. In addition, a semiautomatic photo feature detection method is applied and a set of syntactic patterns is introduced to resolve opinion references. We implemented a prototype system that incorporates the proposed approach and evaluates it on several regions in the World using real data extracted from Flickr

A Visual Analytics System for Cluster Exploration

This paper offers a new way of representing the results of automatic clustering algorithms by employing a Visual Analytics system which maps members of a cluster and their distance to each other onto a twodimensional space. A case study on Urdu complex predicates shows that the system allows for an appropriate investigation of linguistically motivated data.