Automatic Personality Prediction using Deep Learning Based on Social Media Profile Picture and Posts

Nicholaus Hendrik Jeremy, George Christian, Muhammad Fadil Kamal, Derwin Suhartono, Kristien Margi Suryaningrum
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引用次数: 3

Abstract

Uploaded contents by social media users are affected by their personality, for example the profile photo they used and the posts they published. In this research, we create an automatic prediction for Twitter users' personalities through their photo profile and their tweets, comparing the result from using either of the feature and both of them. 1290 Twitter users that had taken MBTI test from 16personalities were used as the dataset. Facial feature from profile photo is obtained by using the face detection model that is combined with smile detection such that not only can we obtain the feature of the face, but also their expressions. As for the color, the feature is obtained by their color composition, which is hue, saturation, and value. For tweets, features are obtained by using a pre-trained word vector. Our result shows that image features can predict personality better than text feature and the combination of text and image features. Based on our result, we also found that a single profile picture is capable of reliably predicting personality.
基于社交媒体头像和帖子的深度学习自动人格预测
社交媒体用户上传的内容受到其个性的影响,例如他们使用的头像和发布的帖子。在这项研究中,我们通过Twitter用户的照片资料和他们的推文创建了一个自动预测Twitter用户性格的功能,并比较使用其中一种功能和同时使用这两种功能的结果。1290名推特用户接受了16个人格的MBTI测试作为数据集。将人脸检测模型与微笑检测相结合,从侧面照片中提取人脸特征,不仅可以得到人脸的特征,还可以得到人脸的表情。对于颜色来说,特征是由它们的颜色组成来获得的,即色调、饱和度和值。对于tweet,特征是通过使用预训练的词向量获得的。我们的研究结果表明,图像特征比文本特征和文本与图像相结合的特征更能预测个性。基于我们的研究结果,我们还发现一张头像能够可靠地预测一个人的性格。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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