Sikström Sverker, Valavičiūtė Ieva, Kuusela Inari, Evors Nicole
Department of Psychology, Lund University, Lund, SE-221 00, Sweden.
Commun Psychol. 2024 May 23;2(1):45. doi: 10.1038/s44271-024-00097-2.
Psychological constructs are commonly quantified with closed-ended rating scales. However, recent advancements in natural language processing (NLP) enable the quantification of open-ended language responses. Here we demonstrate that descriptive word responses analyzed using NLP show higher accuracy in categorizing emotional states compared to traditional rating scales. One group of participants (N = 297) generated narratives related to depression, anxiety, satisfaction, or harmony, summarized them with five descriptive words, and rated them using rating scales. Another group (N = 434) evaluated these narratives (with descriptive words and rating scales) from the author's perspective. The descriptive words were quantified using NLP, and machine learning was used to categorize the responses into the corresponding emotional states. The results showed a significantly higher number of accurate categorizations of the narratives based on descriptive words (64%) than on rating scales (44%), questioning the notion that rating scales are more precise in measuring emotional states than language-based measures.
心理结构通常用封闭式评定量表进行量化。然而，自然语言处理（NLP）的最新进展使得对开放式语言反应的量化成为可能。在此，我们证明，与传统评定量表相比，使用NLP分析的描述性词语反应在对情绪状态进行分类时显示出更高的准确性。一组参与者（N = 297）生成了与抑郁、焦虑、满意度或和谐相关的叙述，用五个描述性词语对其进行总结，并用评定量表对其进行评分。另一组（N = 434）从作者的角度评估这些叙述（包括描述性词语和评定量表）。使用NLP对描述性词语进行量化，并使用机器学习将反应分类到相应的情绪状态中。结果显示，基于描述性词语对叙述进行准确分类的数量（64%）显著高于基于评定量表的数量（44%），这对评定量表在测量情绪状态方面比基于语言的测量方法更精确的观点提出了质疑。
