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Inferring Gender of Micro-Blog Users based on Multi-Classifiers Fusion

Volume 14, Number 2, February 2018, pp. 349-356
DOI: 10.23940/ijpe.18.02.p16.349356

Jinghua Zhenga,*, Shize Guob, Liang Gaob, Di Xuec, Nan Zhaod, Huimin Maa

 aHefei electronic engineering institute, Hefei, 230037, China
bInstitute of North Electronic Equipment, Beijing, 100083, China
cArmy Engineering University, Nanjing, 210007, China
dCAS Institute of Psychology, Beijing, 100101, China


 


 

Abstract:

Knowing user demographic traits offers a great potential for public information. Most research have used local features to predict user demographic traits. Since this method did not make the most of user global features, the prediction performance was low. In this paper, our goal tries to use an ensemble learning method to improve the prediction performance through multi-classifiers fusion. Our work makes three important contributions. Firstly, we show how to predict Sina Micro-blog users’ genders based on his/her text published on the social network. Secondly, we show that user’s personality traits can also be used to infer gender. And last and thirdly, we propose multi-classifiers fusion to predict users’ genders, and give the experimental results that validate our method by comparing it with a different local features dataset. Our experiment demonstrates that our method can improve the accuracy rate, the recall rate of prediction, and the F value.

 

References: 18

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