People use weblogs to express thoughts, present ideas and share knowledge, therefore weblogs are extraordinarily valuable resources, amongs others, for trend analysis. Trends are derived from the chronological sequence of blog post count per topic. The comparison with a reference corpus allows qualitative statements over identified trends. We propose a crosslanguage blog mining and trend visualisation system to analyse blogs across languages and topics. The trend visualisation facilitates the identification of trends and the comparison with the reference news article corpus. To prove the correctness of our system we computed the correlation between trends in blogs and news articles for a subset of blogs and topics. The evaluation corroborated our hypothesis of a high correlation coefficient for these subsets and therefore the correctness of our system for different languages and topics is proven.
[1]
Huan Liu,et al.
Blogosphere: research issues, tools, and applications
,
2008,
SKDD.
[2]
Daniel W. Drezner,et al.
The power and politics of blogs
,
2007
.
[3]
Matthew Hurst,et al.
Deriving marketing intelligence from online discussion
,
2005,
KDD '05.
[4]
Roman Kern,et al.
Crosslanguage Retrieval Based on Wikipedia Statistics
,
2008,
CLEF.
[5]
W. Kienreich,et al.
APA Labs: An experimental web-based platform for the retrieval and analysis of news articles
,
2008,
2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT).
[6]
Maria E. Orlowska,et al.
Robust web content extraction
,
2006,
WWW '06.