research-article

Time-space varying visual analysis of micro-blog sentiment

Authors:

Chenghai Zhang,

Changbo WangAuthors Info & Claims

VINCI '13: Proceedings of the 6th International Symposium on Visual Information Communication and Interaction

Pages 64 - 71

https://doi.org/10.1145/2493102.2493110

Published: 17 August 2013 Publication History

Abstract

Micro-blog sentiment analysis attracts much attention by companies, governments and other organizations. It could help companies to estimate the extent of product acceptance and to determine marketing strategies, governments to monitor online public perception and to improve government-public relation, etc. Researchers mainly focused on time-varying analysis or space varying analysis.

This paper combines time-varying analysis and space varying analysis and proposes an Electron Cloud Model (ECM) based on the Schrodinger equation and Niels Bohr atomic theory to conduct time-varying visual analysis of micro-blog sentiments. In the ECM, an attempt to map a score of sentiment to the electron stability is made. Kernel density estimation and edge bundling are used to conduct space-varying visual analysis of sentiments. The former visualizes sentiment changes in different levels of detail naturally while the latter can reduce visual clutter of edge crossing and reveal high-level edge pattern.

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Cited By

Diehl AHundt MHaussler JSeebacher DChen SCilasun NKeim DShreck T(2018)SocialOcean: Visual Analysis and Characterization of Social Media Bubbles2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)10.1109/BDVA.2018.8534023(1-11)Online publication date: Oct-2018
https://doi.org/10.1109/BDVA.2018.8534023
Kucher KParadis CKerren A(2017)The State of the Art in Sentiment VisualizationComputer Graphics Forum10.1111/cgf.1321737:1(71-96)Online publication date: 12-Jun-2017
https://doi.org/10.1111/cgf.13217
Chen SLin LYuan X(2017)Social Media Visual AnalyticsComputer Graphics Forum10.1111/cgf.1321136:3(563-587)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1111/cgf.13211
Show More Cited By

Index Terms

Time-space varying visual analysis of micro-blog sentiment
1. Applied computing
  1. Document management and text processing
    1. Document capture
      1. Graphics recognition and interpretation
2. Computing methodologies
  1. Computer graphics

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Information

Published In

cover image ACM Other conferences

VINCI '13: Proceedings of the 6th International Symposium on Visual Information Communication and Interaction

August 2013

133 pages

ISBN:9781450319881

DOI:10.1145/2493102

General Chairs:
Jiawan Zhang
Tianjin University, China
,
Gennaro Costagliola
University of Salerno, Italy
,
Program Chair:
Robert P. Biuk-Aghai
University of Macau, Macau

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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TU: Tianjin University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2013

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Research-article

Funding Sources

Ministry of Science and Technology of the People's Republic of China
Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
Shanghai Knowledge Service Platform for Trustworthy Internet of Things
Shanghai Municipal Education Commission

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VINCI '13

Sponsor:

TU

VINCI '13: The 6th International Symposium on Visual Information Communication and Interaction

August 17 - 18, 2013

Tianjin, China

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VINCI '13 Paper Acceptance Rate 12 of 30 submissions, 40%;

Overall Acceptance Rate 71 of 193 submissions, 37%

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Cited By

Diehl AHundt MHaussler JSeebacher DChen SCilasun NKeim DShreck T(2018)SocialOcean: Visual Analysis and Characterization of Social Media Bubbles2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)10.1109/BDVA.2018.8534023(1-11)Online publication date: Oct-2018
https://doi.org/10.1109/BDVA.2018.8534023
Kucher KParadis CKerren A(2017)The State of the Art in Sentiment VisualizationComputer Graphics Forum10.1111/cgf.1321737:1(71-96)Online publication date: 12-Jun-2017
https://doi.org/10.1111/cgf.13217
Chen SLin LYuan X(2017)Social Media Visual AnalyticsComputer Graphics Forum10.1111/cgf.1321136:3(563-587)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1111/cgf.13211
Cao NCui WCao NCui W(2016)Visualizing Sentiments and EmotionsIntroduction to Text Visualization10.2991/978-94-6239-186-4_6(103-114)Online publication date: 23-Oct-2016
https://doi.org/10.2991/978-94-6239-186-4_6
Cao NCui WCao NCui W(2016)Overview of Text Visualization TechniquesIntroduction to Text Visualization10.2991/978-94-6239-186-4_2(11-40)Online publication date: 23-Oct-2016
https://doi.org/10.2991/978-94-6239-186-4_2
Wu YLiu SYan KLiu MWu F(2014)OpinionFlow: Visual Analysis of Opinion Diffusion on Social MediaIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2014.234692020:12(1763-1772)Online publication date: 31-Dec-2014
https://doi.org/10.1109/TVCG.2014.2346920

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