TY - GEN
T1 - Gender identification from e-mails
AU - Cheng, Na
AU - Chen, Xiaoling
AU - Chandramouli, R.
AU - Subbalakshmi, K. P.
PY - 2009
Y1 - 2009
N2 - In this paper, we investigate the topic of gender identification for short length, multi-genre, content-free e-mails. We introduce for the first time (to our knowledge), psycholinguistic and gender-linked cues for this problem, along with traditional stylometric features. Decision tree and Support Vector Machines learning algorithms are used to identify the gender of the author of a given e-mail. The experiment results show that our approach is promising with an average accuracy of 82.2%.
AB - In this paper, we investigate the topic of gender identification for short length, multi-genre, content-free e-mails. We introduce for the first time (to our knowledge), psycholinguistic and gender-linked cues for this problem, along with traditional stylometric features. Decision tree and Support Vector Machines learning algorithms are used to identify the gender of the author of a given e-mail. The experiment results show that our approach is promising with an average accuracy of 82.2%.
UR - http://www.scopus.com/inward/record.url?scp=67650501901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67650501901&partnerID=8YFLogxK
U2 - 10.1109/CIDM.2009.4938643
DO - 10.1109/CIDM.2009.4938643
M3 - Conference contribution
AN - SCOPUS:67650501901
SN - 9781424427659
T3 - 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
SP - 154
EP - 158
BT - 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
T2 - 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009
Y2 - 30 March 2009 through 2 April 2009
ER -