IVITA Workshop Summary Session 1: interactive text analytics (Session chair: Professor Huamin Qu) a) HARVEST: An Intelligent Visual Analytic Tool for the Masses b) A Dynamic Visual Interface for News Stream Analysis c) Visualizing Common Sense Connections with Luminoso Session 2: Space and Time (Session chair: Professor Giuseppe Carenini) a) User Analysis and Visualization from a Semantic Blog System b) Integrating Interactivity into Visualizing Sentiment Analysis of Blogs c) A Visual Approach to Text Corpora Comparison d) Visual Content Correlation Analysis Session 3: Visual text summarization (Session chair: Dr. Shixia Liu) a) An Ontology-based Interface for Improving Information Exploration b) Information Visualization for Corpus Linguistics: Towards Interactive Tools c) Visual Structured Summaries of Human Conversations d) Visual Abstraction and Ordering in Faceted Browsing of Text Collection

IVITA Visual Summary

Important issues that are less covered in most papers Where is the “intelligence” ? In the AI methods to extract information? In the AI to make the interaction adaptive to user goals/query, to the stage of the analysis Who are the target users? Text analysis Experts vs. everyday business users? Detailed characterization of the user tasks Evaluation methodology and metrics What are the pros and cons of the different methods for info extraction and presentation wrt specific textual data and user tasks?

Session 1: interactive text analytics (Session chair: Professor Huamin Qu) 1a) HARVEST: An Intelligent Visual Analytic Tool for the Masses Company project wikis Don’t require users to be infovis experts (everyday business users) Recommend visualizations tailored to analytic context&data Detect user actions / intentions 1b) A Dynamic Visual Interface for News Stream Analysis News, Named Entity Recognition, Opinions (evolution over time) User study – two tasks (seems specific for the tool)

Session 1: interactive text analytics (cont’) (Session chair: Professor Huamin Qu) 1c) Visualizing Common Sense Connections with Luminoso Large quantity of people suggestions / feedback Visualize major clusters (LSA / SVD) with prior knowledge Concept Net View representative Responses Place signposts

Session 2: Space and Time (Session chair: Professor Giuseppe Carenini) 2a) User Analysis and Visualization from a Semantic Blog System User who submits a query is compared with the bloggers who meet the query ?! blog postings mapped in event ontology Temporal and Spatial info is easily extracted. What other info is extracted? For blogs Subject: age, gender… loc.. Object: Book (with subfeatures), Trip (with subfeatures)How? Blog clustering 2b) Integrating Interactivity into Visualizing Sentiment Analysis of Blogs companies virtual document surrogate Time… rate of opinion change

Session 2: Space and Time (cont.) (Session chair: Professor Giuseppe Carenini) 2c) A Visual Approach to Text Corpora Comparison ! Individual archive vs. institutional archive Holistic theme/topic level + drill down into detailed keyword level Stacked graphs to compare two corpora 2d) Visual Content Correlation Analysis Records of emergency room (mix struc/unstruc – text fileds) Topic extraction and evolution Keyword layout Drill-down on structured field (e.g., gender) Investigate content correlation between two unstruc. Fields (e.g. cause of injury and diagnosis)

Session 3: Visual Text Summarization (Session chair: Dr Shixia Liu) 3a) An Ontology-based Interface for Improving Information Exploration News articles, term clouds, ontology (learned) 3b) Information Visualization for Corpus Linguistics: Towards Interactive Tools Task corpus linguistics (corpus between 1415 – 1681) Analysis of POS distributions Novel text viz. are not appropriate for this task

Session 3: Visual Text Summarization (cont.) (Session chair: Dr Shixia Liu) 3c) Visual Structured Summaries of Human Conversations Conversations (mtgs so far); map sentences in ontology; interface to generate extractive summary, combines ontology and whole conversations 3d) Visual Abstraction and Ordering in Faceted Browsing of Text Collection User defined ontology capturing their interest Interface: concepts/terms x doc matrix (concepts are organized in an ontology) “bar” in the cell proportional to relevance of concept to document