CommentVis: Unveiling Comment Insights Through Interactive Visualization Tool.

Guangjing Yan, Jinhwa Jang,Jinwook Seo

IEEE Pacific Visualization Symposium(2024)

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Abstract
Recently, online forums have emerged as a stage for consumers to comment and share their reviews. These user comments serve as a valuable data source for marketing professionals and analysts. Nevertheless, conventional user interfaces often present an overwhelming volume of comments in a linear-structured list, significantly impeding the efficiency of marketing professionals in analyzing the feedback. In response to this challenge, we introduce CommentVis, an interactive visualization tool that helps users grasp the skeleton of comments’ semantic distribution. Using the tool, analysts gain detailed information about comments with profound insights. The tool leverages state-of-the-art large-scale language models, optimizing the speed and depth of analysis of substantial text data that may take a long time for marketers. To illustrate the practical application of CommentVis, we present a usage scenario that demonstrates its effectiveness in real-world marketing analysis. The tool’s impact and utility were further validated through a user study involving three marketing professionals in a global manufacturing company.
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Key words
Human-centered computing—Visualization—Visualization systems and tools—,Computing methodologies—Artificial intelligence—Natural language processing—
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