Title :
Gender identification from E-mails
Author :
Cheng, Na ; Chen, Xiaoling ; Chandramouli, R. ; Subbalakshmi, K.P.
Author_Institution :
Dept. of ECE, Stevens Inst. of Technol., Hoboken, NJ
fDate :
March 30 2009-April 2 2009
Abstract :
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%.
Keywords :
decision trees; electronic mail; learning (artificial intelligence); support vector machines; content-free e-mail; decision tree; gender identification; gender-linked cues; multi-genre e-mail; short length e-mail; stylometric features; support vector machines learning algorithms; Computer mediated communication; Decision trees; Electronic mail; Helium; Internet; Machine learning; Natural languages; Psychology; Support vector machine classification; Support vector machines;
Conference_Titel :
Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2765-9
DOI :
10.1109/CIDM.2009.4938643
