TY - GEN
T1 - LDA based event extraction
T2 - 2nd International Conference on Data Science, ICDS 2015
AU - Li, Jingwei
AU - Huang, Wayne
AU - Chen, Ping
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - As a major public health concern, influenza epidemics causes tens of millions respiratory illnesses worldwide each year. With the development of social network, interaction platform like microblog, is generating massive data providing us a faster and more accurate way to predict the trends in the spread of influenza, which can help us reduce the impact cause by the influenza. The problem of influenza epidemics prediction through Chinese microblog cannot be easily addressed by applying existing approaches and methods, some of which have been used for English documents. Besides, different from traditional text, the microblog is big in volume, update velocity, noise and small in the individual text volume, which cause that traditional deeper semantic analysis method like SVM is inefficient and easy to be over-fitting. To address this problem, we present a deeper semantic analysis to Chinese microblog using a LDA based event extraction framework. Our experiment using 332,886 microblogs from south and north China showed that our method achieved more detailed information extraction about the flu and an earlier flu prediction than the Chinese official ILI data.
AB - As a major public health concern, influenza epidemics causes tens of millions respiratory illnesses worldwide each year. With the development of social network, interaction platform like microblog, is generating massive data providing us a faster and more accurate way to predict the trends in the spread of influenza, which can help us reduce the impact cause by the influenza. The problem of influenza epidemics prediction through Chinese microblog cannot be easily addressed by applying existing approaches and methods, some of which have been used for English documents. Besides, different from traditional text, the microblog is big in volume, update velocity, noise and small in the individual text volume, which cause that traditional deeper semantic analysis method like SVM is inefficient and easy to be over-fitting. To address this problem, we present a deeper semantic analysis to Chinese microblog using a LDA based event extraction framework. Our experiment using 332,886 microblogs from south and north China showed that our method achieved more detailed information extraction about the flu and an earlier flu prediction than the Chinese official ILI data.
UR - https://www.scopus.com/pages/publications/84950981924
UR - https://www.scopus.com/pages/publications/84950981924#tab=citedBy
U2 - 10.1007/978-3-319-24474-7_5
DO - 10.1007/978-3-319-24474-7_5
M3 - Conference contribution
AN - SCOPUS:84950981924
SN - 9783319244730
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 30
EP - 33
BT - Data Science - 2nd International Conference, ICDS 2015, Proceedings
A2 - Yu, Philip S.
A2 - Shi, Yong
A2 - Huang, Wei
A2 - Zhang, Peng
A2 - He, Jing
A2 - Tian, Yingjie
A2 - Zhang, Chengqi
A2 - Zhu, Yangyong
PB - Springer Verlag
Y2 - 8 August 2015 through 9 August 2015
ER -