Western Michigan University, Kalamazoo, MI, USA.
Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI, USA.
Assessment. 2022 Jul;29(5):1075-1085. doi: 10.1177/1073191121996466. Epub 2021 Mar 18.
To date, there is a paucity of research conducting natural language processing (NLP) on the open-ended responses of behavior rating scales. Using three NLP lexicons for sentiment analysis of the open-ended responses of the Behavior Assessment System for Children-Third Edition, the researchers discovered a moderately positive correlation between the human composite rating and the sentiment score using each of the lexicons for strengths comments and a slightly positive correlation for the concerns comments made by guardians and teachers. In addition, the researchers found that as the word count increased for open-ended responses regarding the child's strengths, there was a greater positive sentiment rating. Conversely, as word count increased for open-ended responses regarding child concerns, the human raters scored comments more negatively. The authors offer a proof-of-concept to use NLP-based sentiment analysis of open-ended comments to complement other data for clinical decision making.
迄今为止，针对行为评定量表的开放性反应进行自然语言处理（NLP）的研究还很少。研究人员使用三个 NLP 词汇库对《儿童行为评估系统第三版》的开放性反应进行情感分析，发现人类综合评分与每个词汇库的强项评论的情感评分之间存在中度正相关，而监护人及教师的关注评论则呈略微正相关。此外，研究人员还发现，关于孩子强项的开放性回应的字数增加，情感评分也越高。相反，关于孩子担忧的开放性回应的字数增加，人类评分者对评论的评价就越负面。作者提供了一个基于 NLP 的开放性评论情感分析的概念验证，以补充临床决策的其他数据。
