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Text classification models for personality disorders identification

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Abstract

This research focuses on identifying personality disorders in individuals using their social media text. We developed a unique collection of words (PD-Corpus) and a dataset (PD-TXT), which includes texts marked with different personality disorder traits. Our goal was to classify these texts into six types of personality disorders, using Natural Language Processing (NLP) classification models. The results showed that our transformer-based models, especially the BERT-base-uncased model, were more effective than traditional methods, achieving a 74.7% success rate in correctly classifying these disorders. Also, our models consistently outperform existing literature baseline models on the PD-TXT dataset, showcasing significant enhancements. This study presents a new way to predict personality disorders through linguistic analysis and highlights the potential for further research combining language studies with mental health.

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Data availability

The PD-TXT data are available upon request.

Notes

  1. https://keras.io/.

  2. http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html..

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Acknowledgements

The authors would like to thank the collaboration of the psychologists Dr. Rajat Mitra and Dr. Puneet Jain.

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Authors and Affiliations

  1. Department of Computer Science, Banasthali Vidyapith, Rajasthan 304022, India

    Deepti Jain & C. K. Jha

  2. Department of Computer Engineering, Cummins College of Engineering, Pune, Maharashtra, 411052, India

    Sandhya Arora

  3. Department of Mechanical and Industrial Engineering, Toronto Metropolitan University, 350 Victoria st, Toronto, Ontario, M4C2C3, Canada

    C. K. Jha & Garima Malik

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  1. Deepti Jain
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Correspondence to Deepti Jain.

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Appendix

Appendix

This section includes additional results related to the experiments carried out in this study. Figures 6 and 7 show the training and validation loss and accuracy curves for each deep learning model employed in the experiments. It is evident that the BiLSTM model with Keras embedding converges properly and provides the highest performance across the training epochs.

Fig. 6
figure 6

Loss and accuracy curves for each deep learning model with Keras embeddings. Depicted values on the curve are averaged over fivefold across 10 epochs

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Fig. 7
figure 7

Loss and accuracy curves for each deep learning model with GloVe embeddings. Depicted values on the curve is averaged over fivefold across 10 epochs

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Jain, D., Arora, S., Jha, C.K. et al. Text classification models for personality disorders identification. Soc. Netw. Anal. Min. 14, 64 (2024). https://doi.org/10.1007/s13278-024-01219-8

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