Gender identification from E-mails

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%.

• Type: Conference
• Year: 2009
• Where: CIDM
• Authors: Na Cheng, Xiaoling Chen, R. Chandramouli, K. P. Subbalakshmi
• Keywords: Artificial Intelligence, CIDM 2009, Content-free E-mails, Gender Identification, Machines Learning Algorithms