论文标题
使用合奏深度学习在社交网络上的性别检测
Gender Detection on Social Networks using Ensemble Deep Learning
论文作者
论文摘要
分析社交媒体网站（例如Facebook和Twitter）上不断增加的帖子量需要改进的信息处理方法来分析作者身份。文档分类是此任务的核心，但是随着社交媒体量的增加，传统监督分类器的性能已降低。本文通过合奏分类在性别检测的背景下解决了这个问题，该集合分类采用多模型深度学习体系结构来从不同的特征空间中产生专业的理解。
Analyzing the ever-increasing volume of posts on social media sites such as Facebook and Twitter requires improved information processing methods for profiling authorship. Document classification is central to this task, but the performance of traditional supervised classifiers has degraded as the volume of social media has increased. This paper addresses this problem in the context of gender detection through ensemble classification that employs multi-model deep learning architectures to generate specialized understanding from different feature spaces.
