@inproceedings{szabo-etal-2024-predictive,
    title = "Predictive and Distinctive Linguistic Features in Schizophrenia-Bipolar Spectrum Disorders",
    author = "Szab{\'o}, Martina Katalin  and
      Vincze, Veronika  and
      Dam, Bernadett  and
      Guba, Csenge  and
      Bagi, Anita  and
      Szendi, Istv{\'a}n",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1133",
    pages = "12938--12953",
    abstract = "In this study, we analyze spontaneous speech transcripts from Hungarian patients with schizophrenia, schizoaffective, and bipolar disorders. Our goal is to identify distinctive linguistic features in these patient groups and controls. To our knowledge, no prior study has systematically examined the linguistic features of these disorders or explored their use in distinguishing between these patient groups. We collected recordings from 77 participants during three directed spontaneous speech tasks in a clinical setting, resulting in 458 texts. Our research group manually transcribed the recordings. We processed the written corpus texts using Natural Language Processing methods and tools. The final corpus consists of 179,515 tokens, excluding punctuation. Using this data, we analyze different linguistic features{'} predictive power by computing and comparing their frequency distributions. We then attempt to automatically differentiate between patient groups and controls using our extensive set of linguistic features, employing the random forest algorithm in these experiments. Our results indicate that applying machine learning techniques based on distinctive features can effectively distinguish SZ, SAD, BD, and controls, surpassing baseline results.",
}

In this study, we analyze spontaneous speech transcripts from Hungarian patients with schizophrenia, schizoaffective, and bipolar disorders. Our goal is to identify distinctive linguistic features in these patient groups and controls. To our knowledge, no prior study has systematically examined the linguistic features of these disorders or explored their use in distinguishing between these patient groups. We collected recordings from 77 participants during three directed spontaneous speech tasks in a clinical setting, resulting in 458 texts. Our research group manually transcribed the recordings. We processed the written corpus texts using Natural Language Processing methods and tools. The final corpus consists of 179,515 tokens, excluding punctuation. Using this data, we analyze different linguistic features{'} predictive power by computing and comparing their frequency distributions. We then attempt to automatically differentiate between patient groups and controls using our extensive set of linguistic features, employing the random forest algorithm in these experiments. Our results indicate that applying machine learning techniques based on distinctive features can effectively distinguish SZ, SAD, BD, and controls, surpassing baseline results.
Szab{\'o}, Martina Katalin Vincze, Veronika Dam, Bernadett Guba, Csenge Bagi, Anita Szendi, Istv{\'a}n
Predictive and Distinctive Linguistic Features in Schizophrenia-Bipolar Spectrum Disorders