Human-Computer Interaction – INTERACT 2013

Volume 8118 of the series Lecture Notes in Computer Science pp 116-134

OpinionBlocks: A Crowd-Powered, Self-improving Interactive Visual Analytic System for Understanding Opinion Text

  • Mengdie HuAffiliated withGeorgia Institute of Technology
  • , Huahai YangAffiliated withIBM Almaden Research Center
  • , Michelle X. ZhouAffiliated withIBM Almaden Research Center
  • , Liang GouAffiliated withIBM Almaden Research Center
  • , Yunyao LiAffiliated withIBM Almaden Research Center
  • , Eben HaberAffiliated withIBM Almaden Research Center

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Millions of people rely on online opinions to make their decisions. To better help people glean insights from massive amounts of opinions, we present the design, implementation, and evaluation of OpinionBlocks, a novel interactive visual text analytic system. Our system offers two unique features. First, it automatically creates a fine-grained, aspect-based visual summary of opinions, which provides users with insights at multiple levels. Second, it solicits and supports user interactions to rectify text-analytic errors, which helps improve the overall system quality. Through two crowd-sourced studies on Amazon Mechanical Turk involving 101 users, OpinionBlocks demonstrates its effectiveness in helping users perform real-world opinion analysis tasks. Moreover, our studies show that the crowd is willing to correct analytic errors, and the corrections help improve user task completion time significantly.


Text analytics text visualization self-improving crowd-sourcing