Semantic Web Dog Food

Presented at: Workshop on Visual Interfaces to the Social and the Semantic Web (VISSW2009)

by Franz Wanner, Christian Rohrdantz, Florian Mansmann, Daniela Oelke, Daniel Keim

The technology behind RSS feeds offers great possibilities to retrieve more news items than ever. In contrast to these technical developments, human capabilities to read all these news items have not increased likewise. To bridge this gap, this paper presents a visual analytics tool for conducting semi-automatic sentiment analysis of large news feeds. While the tool automatically retrieves and analyzes RSS feeds with respect to positive and negative opinion words, the more demanding news analysis of finding trends, spotting peculiarities and putting events into context is left to the human expert. For a solid analysis the news similarity filter enables highlighting of similar or redundant news items. A case study about news related to the US presidential election in 2008 shows how the visual interface of the tool empowers the analyst to draw meaningful conclusions without the effort of reading all news postings.

Resource URI on the dog food server: http://data.semanticweb.org/workshop/VISSW/2009/paper/main/6

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