In this study, we applied different text-mining methods to the originality scoring of the Unusual Uses Test (UUT) and Just Suppose Test (JST) from the Torrance Tests of Creative Thinking (TTCT)–Verbal. Responses from 102 and 123 participants who completed Form A and Form B, respectively, were scored using three different text-mining methods. The validity of these scoring methods was tested against TTCT’s manual-based scoring and a subjective snapshot scoring method. Results indicated that text-mining systems are applicable to both UUT and JST items across both forms and students’ performance on those items can predict total originality and creativity scores across all six tasks in the TTCT-Verbal. Comparatively, the text-mining methods worked better for UUT than JST. Of the three text-mining models we tested, the Global Vectors for Word Representation (GLoVe) model produced the most reliable and valid scores. These findings indicate that creativity assessment can be done quickly and at a lower cost using text-mining approaches.

在这项研究中，我们将不同的文本挖掘方法应用于不寻常使用测试 (UUT) 和假设测试 (JST) 的原创性评分，来自创造性思维的托伦斯测试 (TTCT) – 口头。使用三种不同的文本挖掘方法对分别完成表格 A 和表格 B 的 102 和 123 名参与者的回答进行评分。这些评分方法的有效性根据 TTCT 的手动评分和主观快照评分方法进行了测试。结果表明，文本挖掘系统适用于两种形式的 UUT 和 JST 项目，学生在这些项目上的表现可以预测 TTCT-Verbal 中所有六项任务的总原创性和创造力分数。相比之下，文本挖掘方法对 UUT 的效果优于 JST。在我们测试的三个文本挖掘模型中，词表示的全局向量 (GLoVe) 模型产生了最可靠和有效的分数。这些发现表明，可以使用文本挖掘方法以较低的成本快速完成创造力评估。