Word2Vec Approaches in Classifying Schizophrenia Through Speech Pattern

Word2Vec Approaches in Classifying Schizophrenia Through Speech Pattern

Natural Language Processing, Schizophrenia, Speech Pattern, Word2Vec

Schizophrenia is a chronic brain disorder characterized by symptoms such as delusions, hallucinations, and disorganized speech, posing significant challenges for accurate diagnosis. This research investigates an innovative Natural Language Processing (NLP)framework for classifying the speech patterns of schizophrenia patients using Word2Vec, with the aim of determining whether there are significant differences between the two features. The dataset comprises speech transcriptions from 121 schizophrenia patients and 121 non-schizophrenia participants collected through structured interviews. This study compares two Word2Vec architectures, Continuous Bag-of-Words (CBOW) and Skip-Gram (SG), to determine their effectiveness in classifying schizophrenia speech patterns. The results indicate that the SG architecture, with hyperparameter tuning, produces more detailed word representations, particularly for low-frequency words. This approach yields more accurate classification results, achieving an F1-score of 93.81%.These results emphasize the effectiveness of the framework in handling structured and abstract linguistic patterns. By utilizing the advantages of both static and contextual embedding,this approach offers significant potential for clinical applications, providing a reliable tool for improving schizophrenia diagnosis through automated speech analysis.

Putri Alysia Azis1*, Andi Tenriola2, Dewi Fatmarani Surianto3, Nur Azizah Eka Budiarti4, Andi Akram Nur Risal5, Zulhajji

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6323/1039

Department of Informatics and ComputerEngineering, Universitas Negeri Makassar, Indonesia

26-03-2025

FAJAR BAGUS W

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