Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality
Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality
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DOI：
10.1287/isre.2022.1111
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Kai Yang;Raymond Y. K. Lau;A. Abbasi
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作者：
Kai Yang;Raymond Y. K. Lau;A. Abbasi
分析师、经理和政策制定者对能够提供更好前瞻性的预测分析感兴趣。人们普遍认为，在涉及组织政策或消费者决策的预测情景中，包括个性在内的个人特征可能是下游结果的重要预测因素。由于传统的测量机制往往不可行，将个性特征纳入预测模型的工作受到了阻碍。基于文本的人格检测由于数字文本痕迹的公开可获得性而引起了人们的关注，然而IBM、Google、Facebook和学术研究提出的最先进的模型并不够准确，不足以用于下游的真实世界预测任务。我们提出了一种新的基于文本的人格测量方法，相对于工业界和学术界开发的最好的现有方法，该方法将人格维度的检测提高了10-20个百分点。利用金融和卫生领域的案例研究，我们表明，更准确的基于文本的个性检测可以转化为下游应用程序的显著改进，例如预测未来的公司业绩或预测大流行感染率。我们的发现对于专注于启用、产生或使用预测性分析以提高决策敏捷性的管理人员具有重要意义。
Analysts, managers, and policymakers are interested in predictive analytics capable of offering better foresight. It is generally accepted that in forecasting scenarios involving organizational policies or consumer decision making, personal characteristics, including personality, may be an important predictor of downstream outcomes. The inclusion of personality features in forecasting models has been hindered by the fact that traditional measurement mechanisms are often infeasible. Text-based personality detection has garnered attention due to the public availability of digital textual traces, however state-of-the-art models proposed by IBM, Google, Facebook, and academic research are not accurate enough to be used for downstream real-world forecasting tasks. We propose a novel text-based personality measurement approach that improves detection of personality dimensions by 10–20 percentage points relative to the best existing methods developed in industry and academia. Using case studies in the finance and health domains, we show that more accurate text-based personality detection can translate into significant improvements in downstream applications such as forecasting future firm performance or predicting pandemic infection rates. Our findings have important implications for managers focused on enabling, producing, or consuming predictive analytics for enhanced agility in decision making.