Authors:
Miloš Krstajić
1
;
Peter Bak
1
;
Daniela Oelke
1
;
Daniel A. Keim
1
;
Martin Atkinson
2
and
William Ribarsky
3
Affiliations:
1
University of Konstanz, Germany
;
2
European Commission’s Joint Research Center, Italy
;
3
UNC Charlotte Visualization Center, United States
Keyword(s):
News feed application, Sentiment analysis, Spatiotemporal analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Society, e-Business and e-Government
;
Soft Computing
;
Symbolic Systems
;
Web Information Systems and Technologies
;
Web Mining
Abstract:
This paper presents a visual analytics approach to explore large news article collections in the domains of polarity and spatial analysis. The exploration is performed on the data collected with Europe Media Monitor (EMM), a system which monitors over 2500 online sources and processes 90,000 articles per day. By analyzing the news feeds, we want to find out which topics are important in different countries and what is the general polarity of the articles within these topics. To assess the polarity of a news article, automatic techniques for polarity analysis are employed and the results are represented using Literature Fingerprinting for visualization. In the spatial description of the news feeds, every article can be represented by two geographic attributes, the news origin and the location of the event itself. In order to assess these spatial properties of news articles, we conducted our geo-analysis, which is able to cope with the size and spatial distribution of the data. %These
data are characterized by high overlapping and inefficient use of space. Spatial analysis of the news articles data employs Pixel Placement and Cartograms technique to deal with these challenges. Within this application framework, we show opportunities how real-time news feed data can be analyzed efficiently.
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