Question-based computational language approach outperforms rating scales in quantifying emotional states.

ABSTRACT: 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

SUBMITTER: Sikstrom S 

PROVIDER: S-EPMC11332055 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature