TY  - CONF
T1  - BEHAVIOR THEORY ENABLED GENDER CLASSIFICATION METHOD
T2  - the Fifteeth workshop on e-Business (WeB 2016) in Dublin
Y1  - 2017
A1  - Wang,Jing
A1  - Yan,Xiangbin
A1  - Zhu,Bin
KW  - BIS
KW  - Business Analytics
JA  - the Fifteeth workshop on e-Business (WeB 2016) in Dublin
U2  - b
U4  - 145142386688
ID  - 145142386688
ER  - 

TY  - ABST
T1  - Gender Classification for Product Reviewers in China: A Data-Driven Approach
Y1  - 2013
A1  - Zhu,Bin
A1  - Yan,Xiangbin
A1  - Wang,Jing
KW  - BIS
KW  - Business Analytics
AB  - While it is crucial for organizations to automatically identify the gender of participants in product discussion forums, they may have difficulties adopting existing gender classification methods because the associations between the linguistic features used in those studies and gender type usually varies with context. The prototype system we propose to demo validates a framework for the development of gender classification that uses a more “data-driven” approach. It constantly extracts content-specific features from the discussion content. And the system could automatically adjust itself to accommodate the contextual changes in order to achieve better classification accuracy.
UR  - http://www.som.buffalo.edu/isinterface/wits2013/
U2  - d
U4  - 88335765504
ID  - 88335765504
ER  -