![]() ![]() The first line of research is the linguistic features that characterize what people with different psychological states are interested in and experiencing ( Tadesse et al., 2019 Jones et al., 2020). Previous studies have analyzed the linguistic features of texts composed by individuals with psychological issues. ![]() These studies may complement previous studies and facilitate the identification of psychological states. The main rationale of such line of research is that an individual’s psychological state impacts the language used to represent his/her emotions, feelings, and thoughts ( Wolohan et al., 2018 Scourfield et al., 2019). The language pertinent to mental health has recently emerged as an area of particular interest ( Sun et al., 2020). Significance and suggestions of the study are also offered. The study represents one of the first attempts 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. The results showed that the proposed linguistic features with machine-learning algorithms, namely Support Vector Machine and Deep Learning achieved a high level of performance in the detection of psychological state. A large dataset of forum posts including texts of anxiety, depression, suicide ideation, and normal states were experimented with machine-learning algorithms. In this study, we proposed using additional linguistic features, that is, sentiments polarities and emotions, to classify texts of various psychological states. 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.
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