TextVista: NLP-Enriched Time-Series Text Data Visualizations
Authors: 
Fateme Rajabiyazdi, 
Shri Harini Ramesh, Beck Langstone, Daniil Kulik, Justin PontalbaAuthors Info & Claims
Article No.: 12, Pages 1 - 12
Abstract
There is a vast amount of unstructured text data generated every day analyzing and making sense of these text-based datasets is a complex, cumbersome task. The existing visualization tools that analyze text data leveraging Natural Language Processing (NLP) techniques, are often tailored for structured text-based data. They also fail to support reading, a crucial analysis task to validate the output of NLP techniques. We designed and developed TextVista, an NLP-enriched visualization tool that supports analysts during their analysis of unstructured text with temporal references. Our tool combines techniques including clustering, sentiment analysis, and threat detection with three views that visualize high-level patterns in the data to encourage reading. We report on TextVista’s iterative design process, which included a focus group to distill design requirements, a think-aloud interview study with data analysts to understand their impressions of the tool, and a diary study to assess its long-term usage. Through this process, we identified how TextVista supported the analysis of unstructured text with temporal references using NLP techniques and fostered methods to promote reading in situ. TextVista also encouraged serendipity when analyzing data via its question-focused overviews and flexible avenues to explore data.
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Index Terms
- TextVista: NLP-Enriched Time-Series Text Data Visualizations
Index terms have been assigned to the content through auto-classification.
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437 pages
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DOI:10.1145/3670947
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