Linguistic features and psychological states: A machine-learning based approach - Du, Xiaowei; Sun, Yunmei (Frontiers in Psychology, 2022)
Previous research mostly used simplistic measures and limited linguistic features (e.g., personal pronouns, absolutist words, and sentiment words) in a text to identify its author’s psychological states. In this study, we proposed using additional linguistic features, such as sentiments polarities and emotions, to classify texts of various psychological states. A large dataset of forum posts including texts of anxiety, depression, suicide ideation, and normal states were experimented with machine-learning algorithms. The results showed that the proposed linguistic features with machine-learning algorithms such as Support Vector Machine and Deep Learning achieved a high level of performance in the detection of psychological state. The study is probably the first attempt that uses sentiment polarities and emotions to detect texts of psychological states, and the findings may contribute to our understanding of how accuracy may be enhanced in the detection of various psychological states. Significance and suggestions of the study are also offered.