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| Social Network User Gender Recognition by Combining Text and Emoji Features |
| WANG Hao, XU Xiaoke
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| College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China |
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Abstract In order to improve the accuracy of gender recognition for social network users, the text features and emoticon features of a single user are fused to identify the user's gender, and then the interactive feature information of multiple users is extracted to further improve the accuracy of gender recognition. The experimental results show that the accuracy of user gender recognition is improved by 6.8% after the fusion of multi-user interaction features. It shows that emoticons and multi-user interaction features are very helpful to improve the accuracy of user gender identification, and improve the accuracy of gender information identification of social network users.
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Received: 20 July 2021
Published: 09 January 2023
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