Gender identification from e-mails

Na Cheng, Xiaoling Chen, R. Chandramouli, K. P. Subbalakshmi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

41 Scopus citations

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%.

Original languageEnglish
Title of host publication2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
Pages154-158
Number of pages5
DOIs
StatePublished - 2009
Event2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Nashville, TN, United States
Duration: 30 Mar 20092 Apr 2009

Publication series

Name2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings

Conference

Conference2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009
Country/TerritoryUnited States
CityNashville, TN
Period30/03/092/04/09

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