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.
王浩, 许小可. 融合文本和表情符号特征的社交网络用户性别识别[J]. 复杂系统与复杂性科学, 2022, 19(4): 17-24.
WANG Hao, XU Xiaoke. Social Network User Gender Recognition by Combining Text and Emoji Features. Complex Systems and Complexity Science, 2022, 19(4): 17-24.
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