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Tiara: a visual exploratory text analytic system

By Furu Wei, Shixia Liu, Yangqiu Song, Shimei Pan, Michelle X. Zhou, Weihong Qian, Lei Shi, Li Tan and Qiang Zhang


In this paper, we present a novel exploratory visual analytic system called TIARA (Text Insight via Automated Responsive Analytics), which combines text analytics and interactive visualization to help users explore and analyze large collections of text. Given a collection of documents, TIARA first uses topic analysis techniques to summarize the documents into a set of topics, each of which is represented by a set of keywords. In addition to extracting topics, TIARA derives time-sensitive keywords to depict the content evolution of each topic over time. To help users understand the topic-based summarization results, TIARA employs several interactive text visualization techniques to explain the summarization results and seamlessly link such results to the original text. We have applied TIARA to several real-world applications, including email summarization and patient record analysis. To measure the effectiveness of TIARA, we have conducted several experiments. Our experimental results and initial user feedback suggest that TIARA is effective in aiding users in their exploratory text analytic tasks. Categories and Subject Descriptor

Topics: Algorithms, Experimentation Keywords Visual text analytics, exploratory analytics, document summarization, topic
Publisher: ACM
Year: 2010
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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