The use of open-ended survey questions for data collection has increased significantly across various areas, as has the application of machine learning (ML) and natural language processing (NLP) techniques to analyze respondents’ opinions. In this study, we conducted a scoping review of 79 studies that analyze open-ended answers given in surveys. We structured our review around six main criteria: application of supervised learning, unsupervised learning, Supervised Descriptive Rule Discovery (SDRD), open-ended questions, NLP, and opinion comparison. This approach allowed us to identify the most used tasks, algorithms, and technologies in ML and NLP, revealing areas of opportunity and the main future challenges. We based our review on the methodological framework of Arksey and O’Malley and adapted PRISMA for reporting systematic reviews. Our findings suggest that most studies addressing surveys with open-ended questions were published in 2020 and 2022, predominantly focusing on research and health domains.
Olmos-Vallejo et al. (Tue,) studied this question.