استفاده از متن برای پیش بینی سلامت روانی و جسمی: ​​مقایسۀ رؤسای انسان و تجزیه و تحلیل متن کامپیوتری

Given the wide-spread use of social media, text analysis has emerged as a promising way to gather information about individuals. However, it is still unclear which method of text analysis is best for determining different types of information. This study compared the utility of automated text analysis (LIWC) with human raters in predicting self-reported psychological and physical health. Expressive writing essays from chronic pain patients were used from a previous online intervention study. Results indicate that human ratings added predictive power above and beyond the LIWC on measures of depression. However, the LIWC was almost as proficient as human raters when predicting pain catastrophizing and illness intrusiveness. Neither the LIWC nor human ratings were good predictors of pain severity and life satisfaction. Overall the utility of automated text analysis over human raters depends on the individual characteristic being measured.