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Title: Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams

Abstract

Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction, Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.

Authors:
 [1];  [1];  [1];  [1];  [2]
  1. ORNL
  2. Google Inc.
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1225428
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Conference on Big Data, Santa Clara, CA, USA, 20151029, 20151101
Country of Publication:
United States
Language:
English
Subject:
social media; visual analytics; data visualization; emotion; sentiment; machine learning

Citation Formats

Steed, Chad A, Drouhard, Margaret MEG G, Beaver, Justin M, Pyle, Joshua M, and BogenII, Paul L. Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams. United States: N. p., 2015. Web.
Steed, Chad A, Drouhard, Margaret MEG G, Beaver, Justin M, Pyle, Joshua M, & BogenII, Paul L. Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams. United States.
Steed, Chad A, Drouhard, Margaret MEG G, Beaver, Justin M, Pyle, Joshua M, and BogenII, Paul L. 2015. "Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams". United States. https://www.osti.gov/servlets/purl/1225428.
@article{osti_1225428,
title = {Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams},
author = {Steed, Chad A and Drouhard, Margaret MEG G and Beaver, Justin M and Pyle, Joshua M and BogenII, Paul L.},
abstractNote = {Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction, Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.},
doi = {},
url = {https://www.osti.gov/biblio/1225428}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}

Conference:
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