TY - GEN
T1 - Hot topic detection in local areas using Twitter and Wikipedia
AU - Ishikawa, Shota
AU - Arakawa, Yutaka
AU - Tagashira, Shigeaki
AU - Fukuda, Akira
PY - 2012
Y1 - 2012
N2 - As microblog services become increasingly popular, spatial-temporal text data has increased explosively. Many studies have proposed methods to spatially and temporally analyze an event, indicated by the text data. These studies have aimed a extracting the period and the location in which a specified topic frequently occurs. In this paper, we focus on a system that detects hot topic in a local area and during a particular period. There can be a variation in the words used even though the posts are essentially about the same hot topic. We propose a classification method that mitigates the variation of posted words related to the same topic.
AB - As microblog services become increasingly popular, spatial-temporal text data has increased explosively. Many studies have proposed methods to spatially and temporally analyze an event, indicated by the text data. These studies have aimed a extracting the period and the location in which a specified topic frequently occurs. In this paper, we focus on a system that detects hot topic in a local area and during a particular period. There can be a variation in the words used even though the posts are essentially about the same hot topic. We propose a classification method that mitigates the variation of posted words related to the same topic.
UR - http://www.scopus.com/inward/record.url?scp=84864225808&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864225808&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84864225808
SN - 9781467319133
T3 - ARCS Workshops, ARCS 2012
BT - ARCS Workshops, ARCS 2012
T2 - 2012 International Conference on Architecture of Computing Systems, ARCS 2012
Y2 - 28 February 2012 through 2 March 2012
ER -