Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach.

Introduction: The diagnostic boundaries between schizophrenia and bipolar disorder are controversial due to the ambiguity of psychiatric nosology. From this perspective, it is noteworthy that formal thought disorder has historically been considered pathognomonic of schizophrenia. Given that human thought is partially based on language, we can hypothesize that alterations in language may help differentiate between schizophrenia and bipolar disorder.

Method: In this exploratory study, we employed natural language processing techniques to identify differences in language abnormalities between patients with schizophrenia and bipolar disorder. The KoBERT and KoGPT language models were used to determine sentence acceptability, assessing how natural and therefore acceptable a given sentence is to the general population. In addition, semantic word networks were constructed for each group, and network measures were compared.

Results: Patients with schizophrenia or bipolar disorder used less acceptable sentences than controls. Post hoc analysis revealed that the schizophrenia group used less acceptable sentences than the bipolar disorder group. Furthermore, the semantic word networks of the three groups were significantly different in the three network measures. Post hoc analysis revealed a significant difference between the schizophrenia and bipolar disorder networks. The bipolar disorder network generally fell between the schizophrenia and control networks, except in terms of the average clustering coefficient.

Conclusions: Patients with schizophrenia and bipolar disorder showed significant differences in sentence acceptability as calculated by the language model, as well as in the network metrics estimated by semantic network analysis. Thus, language abnormalities may represent surrogate markers of thought disorders and help differentiate between schizophrenia and bipolar disorder.