One’s personality is widely accepted as an indicator of job performance, job satisfaction and tenure intention. The ability to measure an applicant’s personality in the selection process helps recruiters, hiring managers and the applicant make better hiring decisions. Our work shows that textual content of answers to standard interview questions related to past behaviour and situational judgement can be used to reliably infer personality traits. We used data from over 46,000 job applicants who completed an online chat interview that also included a personality questionnaire based on the six-factor HEXACO personality model to self-rate their personality. Using natural language processing (NLP) and machine learning methods we built a regression model to infer HEXACO trait values from textual content. We compared the performance of five different text representation methods and found that term frequency-inverse document frequency (TF-IDF) with Latent Dirichlet Allocation (LDA) topics performed the best with an average correlation of r = 0.39. As a comparison, a large study of Facebook messages based inference of Big 5 personality found an average correlation of r = 0.35 and IBM’s Personality Insights service built using twitter text data reports an average correlation of r = 0.31. We further validated our model with a group of 117 volunteers who used an agreement scale of yes/no/maybe to rate the individual trait descriptors generated based on the model outcomes. On average, 87.83% of the participants agreed with the personality description given for each of the six traits. The ability of algorithms to objectively infer a candidate’s personality using only the textual content of interview answers presents significant opportunities to remove the subjective biases involved in human interviewer judgement of candidate personality.

中文翻译：

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使用开放式面试问题的答案预测性格

一个人的个性被广泛接受为工作绩效、工作满意度和任期意向的指标。在选拔过程中衡量申请人个性的能力有助于招聘人员、招聘经理和申请人做出更好的招聘决定。我们的工作表明，与过去行为和情境判断相关的标准面试问题答案的文本内容可用于可靠地推断人格特征。我们使用了超过 46,000 名完成在线聊天面试的求职者的数据，其中还包括基于六因素 HEXACO 人格模型的人格问卷，以对他们的人格进行自我评价。我们使用自然语言处理 (NLP) 和机器学习方法构建了一个回归模型，以从文本内容推断 HEXACO 特征值。我们比较了五种不同文本表示方法的性能，发现词频-逆文档频率 (TF-IDF) 和潜在狄利克雷分配 (LDA) 主题表现最好，平均相关性为 r = 0.39。作为比较，一项基于 Facebook 消息的大型 5 大人格推断的大型研究发现平均相关性为 r = 0.35，而使用 Twitter 文本数据构建的 IBM Personality Insights 服务报告的平均相关性为 r = 0.31。我们通过一组 117 名志愿者进一步验证了我们的模型，他们使用是/否/可能的同意量表来评估基于模型结果生成的个体特征描述符。平均而言，87.83% 的参与者同意为六个特征中的每一个给出的个性描述。