Results and Challenges in Visualizing Analytic Provenance of TextAnalysis Tasks Using Interaction Logs

Abstract

After data analysis, recalling and communicating the steps and rationale followed during the analysis can be difficult. This paper explores the use of interaction logs to generate summaries of an analyst’s interest based on interactions with specific data items in a text analysis scenario. Our approach uses data-interaction events as a proxy for user interest in and experience of information. Logging can produce verbose logs that detail all available readable content, so the discussed approach uses topic modeling (LDA) over different time segments to summarize the verbose information and generate visualizations of the history of user interest. Our preliminary results motivate a discussion on potential benefits and challenges of using interaction data to generate provenance visualizations for text analytics.