Time-space varying visual analysis of micro-blog sentiment

  • Chenghai Zhang
  • , Yuhua Liu
  • , Changbo Wang*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

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.

Original languageEnglish
Title of host publicationVINCI 2013 - 6th International Symposium on Visual Information Communication and Interaction
Pages64-71
Number of pages8
DOIs
StatePublished - 2013
Event6th International Symposium on Visual Information Communication and Interaction, VINCI 2013 - Tianjin, China
Duration: 17 Aug 201318 Aug 2013

Publication series

NameACM International Conference Proceeding Series

Conference

Conference6th International Symposium on Visual Information Communication and Interaction, VINCI 2013
Country/TerritoryChina
CityTianjin
Period17/08/1318/08/13

Keywords

  • edge bundling
  • electron cloud model
  • kernel density
  • micro-blog sentiments
  • sentiment analysis
  • visualization

Fingerprint

Dive into the research topics of 'Time-space varying visual analysis of micro-blog sentiment'. Together they form a unique fingerprint.

Cite this