TY - GEN
T1 - Matisse
T2 - 3rd IEEE International Conference on Big Data, IEEE Big Data 2015
AU - Steed, Chad A.
AU - Drouhard, Margaret
AU - Beaver, Justin
AU - Pyle, Joshua
AU - Bogen, Paul L.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84963760042&partnerID=8YFLogxK
U2 - 10.1109/BigData.2015.7363826
DO - 10.1109/BigData.2015.7363826
M3 - Conference contribution
AN - SCOPUS:84963760042
T3 - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
SP - 807
EP - 814
BT - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
A2 - Luo, Feng
A2 - Ogan, Kemafor
A2 - Zaki, Mohammed J.
A2 - Haas, Laura
A2 - Ooi, Beng Chin
A2 - Kumar, Vipin
A2 - Rachuri, Sudarsan
A2 - Pyne, Saumyadipta
A2 - Ho, Howard
A2 - Hu, Xiaohua
A2 - Yu, Shipeng
A2 - Hsiao, Morris Hui-I
A2 - Li, Jian
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 October 2015 through 1 November 2015
ER -