Mennatallah El-Assady

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

We introduce a novel visual analytics approach to analyze speaker behavior patterns in multi‐party conversations. We propose Topic‐Space Views to track the movement of speakers across the thematic landscape of a conversation. Our tool is designed to assist political science scholars in exploring the dynamics of a conversation over time to generate and prove hypotheses about speaker interactions and behavior patterns. Moreover, we introduce a glyph‐based representation for each speaker turn based on linguistic and statistical cues to abstract relevant text features. We present animated views for exploring the general behavior and interactions of speakers over time and interactive steady visualizations for the detailed analysis of a selection of speakers. Using a visual sedimentation metaphor we enable the analysts to track subtle changes in the flow of a conversation over time while keeping an overview of all past speaker turns. We evaluate our approach on real‐world datasets and the results have been insightful to our domain experts.

Exploratory Text Analysis using Lexical Episode Plots

In this paper, we present Lexical Episode Plots, a novel automated text-mining and visual analytics approach for exploratory text analysis. In particular, we first describe an algorithm for automatically annotating text regions to examine prominent themes within natural language texts. The algorithm is based on lexical chaining to find spans of text in which the frequency of a term is significantly higher than its average in the document. In a second step we present an interactive visualization supporting the exploration and interpretation of Lexical Episodes. The visualization links higher-level thematic structures with content-level details. The methodological capabilities of our approach are illustrated by analyzing the televised US presidential election debates.

NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations

We present NEREx, an interactive visual analytics approach for the exploratory analysis of verbatim conversational transcripts. By revealing different perspectives on multi‐party conversations, NEREx gives an entry point for the analysis through high‐level overviews and provides mechanisms to form and verify hypotheses through linked detail‐views. Using a tailored named‐entity extraction, we abstract important entities into ten categories and extract their relations with a distance‐restricted entity‐relationship model. This model complies with the often ungrammatical structure of verbatim transcripts, relating two entities if they are present in the same sentence within a small distance window. Our tool enables the exploratory analysis of multi‐party conversations using several linked views that reveal thematic and temporal structures in the text. In addition to distant‐reading, we integrated close‐reading views for a text‐level investigation process. Beyond the exploratory and temporal analysis of conversations, NEREx helps users generate and validate hypotheses and perform comparative analyses of multiple conversations. We demonstrate the applicability of our approach on real‐world data from the 2016 U.S. Presidential Debates through a qualitative study with three domain experts from political science.

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.

Visual linguistic analysis of political discussions: Measuring deliberative quality

This article reports on a Digital Humanities research project which is concerned with the automated linguistic and visual analysis of political discourses with a particular focus on the concept of deliberative communication. According to the theory of deliberative communication as discussed within political science, political debates should be inclusive and stakeholders participating in these debates are required to justify their positions rationally and respectfully and should eventually defer to the better argument. The focus of the article is on the novel interactive visualizations that combine linguistic and statistical cues to analyze the deliberative quality of communication automatically. In particular, we quantify the degree of deliberation for four dimensions of communication: Participation, Respect, Argumentation and Justification, and Persuasiveness. Yet, these four dimensions have not been linked within a combined linguistic and visual framework, but each single dimension helps determining the degree of deliberation independently from each other. Since at its core, deliberation requires sustained and appropriate modes of communication, our main contribution is the automatic annotation and disambiguation of causal connectors and discourse particles.

Bridging Text Visualization and Mining : A Task-Driven Survey

Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and 4,346 mining papers published between 1992-2017 in two fields: visualization and text mining. From the analysis, we derived around 300 concepts (visualization techniques, mining techniques, and analysis tasks) and built a taxonomy for each type of concept. The co-occurrence relationships between the concepts were also extracted. Our research can be used as a stepping-stone for other researchers to 1) understand a common set of concepts used in this research topic; 2) facilitate the exploration of the relationships between visualization techniques, mining techniques, and analysis tasks; 3) understand the current practice in developing visual text analytics tools; 4) seek potential research opportunities by narrowing the gulf between visualization and mining techniques based on the analysis tasks; and 5) analyze other interdisciplinary research areas in a similar way. We have also contributed a web-based visualization tool for analyzing and understanding research trends and opportunities in visual text analytics.

Interactive Visual Analysis of Transcribed Multi-Party Discourse.

We present the first web-based Visual Analytics framework for the analysis of multi-party discourse data using verbatim text transcripts. Our framework supports a broad range of server-based processing steps, ranging from data mining and statistical analysis to deep linguistic parsing of English and German. On the client-side, browser-based Visual Analytics components enable multiple perspectives on the analyzed data. These interactive visualizations allow exploratory content analysis, argumentation pattern review and speaker interaction modeling.

Feature Alignment for the Analysis of Verbatim Text Transcripts

In the research of deliberative democracy, political scientists are interested in analyzing the communication models of discussions, debates, and mediation processes with the goal of extracting reoccurring discourse patterns from the verbatim transcripts of these conversations. To enhance the time-exhaustive manual analysis of such patterns, we introduce a visual analytics approach that enables the exploration and analysis of repetitive feature patterns over parallel text corpora using feature alignment. Our approach is tailored to the requirements of our domain experts. In this paper, we discuss our visual design and workflow, and we showcase the applicability of our approach using an experimental parallel corpus of political debates.

Visual Text Analytics: Techniques for Linguistic Information Visualization

Visual Text Analytics has been an active area of interdisciplinary research (http://textvis.lnu.se/). This interactive tutorial is designed to give attendees an introduction to the area of information visualization, with a focus on linguistic visualization. After an introduction to the basic principles of information visualization and visual analytics, this tutorial will give an overview of the broad spectrum of linguistic and text visualization techniques, as well as their application areas [3]. This will be followed by a hands-on session that will allow participants to design their own visualizations using tools (e.g., Tableau), libraries (e.g., d3.js), or applying sketching techniques [4]. Some sample datasets will be provided by the instructor. Besides general techniques, special access will be provided to use the VisArgue framework [1] for the analysis of selected datasets.

Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution

To effectively assess the potential consequences of human interventions in model-driven analytics systems, we establish the concept of speculative execution as a visual analytics paradigm for creating user-steerable preview mechanisms. This paper presents an explainable, mixed-initiative topic modeling framework that integrates speculative execution into the algorithmic decision-making process. Our approach visualizes the model-space of our novel incremental hierarchical topic modeling algorithm, unveiling its inner-workings. We support the active incorporation of the users domain knowledge in every step through explicit model manipulation interactions. In addition, users can initialize the model with expected topic seeds, the backbone priors. For a more targeted optimization, the modeling process automatically triggers a speculative execution of various optimization strategies, and requests feedback whenever the measured model quality deteriorates. Users compare the proposed optimizations to the current model state and preview their effect on the next model iterations, before applying one of them. This supervised human-in-the-Ioop process targets maximum improvement for minimum feedback and has proven to be effective in three independent studies that confirm topic model quality improvements.