skip to main content

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

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.
 [1] ;  [1] ;  [2] ;  [1] ;  [1]
  1. ORNL
  2. Google Inc.
Publication Date:
OSTI Identifier:
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: ACM IUI Workshop on Visual Text Analytics, Atlanta, GA, USA, 20150329, 20150329
Research Org:
Oak Ridge National Laboratory (ORNL)
Sponsoring Org:
ORNL LDRD Director's R&D
Country of Publication:
United States
visualization; visual analytics; interaction; social media; machine learning; sentiment analysis; opinion mining