OpinionBlocks: A Crowd-Powered, Self-improving Interactive Visual Analytic System for Understanding Opinion Text - Human-Computer Interaction – INTERACT 2013 Access content directly
Conference Papers Year : 2013

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

Mengdie Hu
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  • PersonId : 1005594
Huahai Yang
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  • PersonId : 1005595
Michelle X. Zhou
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  • PersonId : 1005596
Liang Gou
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  • PersonId : 1005597
Yunyao Li
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  • PersonId : 1005598
Eben Haber
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  • PersonId : 1005599

Abstract

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.
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hal-01501792 , version 1 (04-04-2017)

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Mengdie Hu, Huahai Yang, Michelle X. Zhou, Liang Gou, Yunyao Li, et al.. OpinionBlocks: A Crowd-Powered, Self-improving Interactive Visual Analytic System for Understanding Opinion Text. 14th International Conference on Human-Computer Interaction (INTERACT), Sep 2013, Cape Town, South Africa. pp.116-134, ⟨10.1007/978-3-642-40480-1_8⟩. ⟨hal-01501792⟩
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