Text Stream Trend Analysis using Multiscale Visual Analytics with Applications to Social Media Systems

Conference · Thu Jan 01 00:00:00 EST 2015

OSTI ID:1185946

Steed, Chad A ^[1]; Beaver, Justin M ^[1]; BogenII, Paul L. ^[2]; Drouhard, Margaret MEG G ^[1]; Pyle, Joshua M ^[1]

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1. ORNL
2. Google Inc.

In this paper, we introduce a new visual analytics system, called Matisse, that allows exploration of global trends in textual information streams with specific application to social media platforms. Despite the potential for real-time situational awareness using these services, interactive analysis of such semi-structured textual information is a challenge due to the high-throughput and high-velocity properties. Matisse addresses these challenges through the following contributions: (1) robust stream data management, (2) automated sen- timent/emotion analytics, (3) inferential temporal, geospatial, and term-frequency visualizations, and (4) a flexible drill-down interaction scheme that progresses from macroscale to microscale views. In addition to describing these contributions, our work-in-progress paper concludes with a practical case study focused on the analysis of Twitter 1% sample stream information captured during the week of the Boston Marathon bombings.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 1185946
• Resource Relation: Conference: ACM IUI Workshop on Visual Text Analytics, Atlanta, GA, USA, 20150329, 20150329
• Country of Publication: United States
• Language: English

Related Subjects

visualization
visual analytics
interaction
social media
machine learning
sentiment analysis
opinion mining