As a contribution to personality detection in languages other than English, we rely on distant supervision to create Personal-ITY, a novel corpus of YouTube comments in Italian, where authors are labelled with personality traits. The traits are derived from one of the mainstream personality theories in psychology research, named MBTI. Using personality prediction experiments, we (i) study the task of personality prediction in itself on our corpus as well as on TwiSty, a Twitter dataset also annotated with MBTI labels; (ii) carry out an extensive, in-depth analysis of the features used by the classifier, and view them specifically under the light of the original theory that we used to create the corpus in the first place. We observe that no single model is best at personality detection, and that while some traits are easier than others to detect, and also to match back to theory, for other, less frequent traits the picture is much more blurred.

使用 distant supervision 方法创建了一个新的 YouTube 评论语料库，标注了作者的人格特质，任务是在此语料库上进行人格预测实验以及与带有 MBTI 标注的推特数据集 TwiSty 上的实验进行对比。通过对分类器使用的特征进行深入分析，发现不同的模型在人格检测上表现都不一，有些特质更容易被检测出，而有些特质则更加模糊。

利用个性化方法匹配理论和数据：意大利YouTube评论语料库揭示的个性特征