Evaluating text coherence based on the graph of the consistency of phrases to identify symptoms of schizophrenia

TL;DR

This paper proposes a novel graph-based method analyzing phrase consistency to evaluate text coherence, aiding in the detection of schizophrenia symptoms through linguistic feature analysis.

Contribution

It introduces a new approach using phrase consistency graphs for assessing text coherence in schizophrenia detection, incorporating multiple linguistic features for classifier training.

Findings

The method effectively distinguishes schizophrenia-related speech patterns.

Linguistic features significantly influence classification accuracy.

The approach shows potential for broader mental health text analysis tasks.

Abstract

Different state-of-the-art methods of the detection of schizophrenia symptoms based on the estimation of text coherence have been analyzed. The analysis of a text at the level of phrases has been suggested. The method based on the graph of the consistency of phrases has been proposed to evaluate the semantic coherence and the cohesion of a text. The semantic coherence, cohesion, and other linguistic features (lexical diversity, lexical density) have been taken into account to form feature vectors for the training of a model-classifier. The training of the classifier has been performed on the set of English-language interviews. According to the retrieved results, the impact of each feature on the output of the model has been analyzed. The results obtained can indicate that the proposed method based on the graph of the consistency of phrases may be used in the different tasks of the detection of mental illness.