@inproceedings{buyanov-etal-2026-psytechlab,
    title = "psytechlab at {CLP}sych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis",
    author = "Buyanov, Igor  and
      Valieva, Nafisa  and
      Mazurina, Ekaterina",
    editor = "Zirikly, Aya  and
      Bar, Kfir  and
      MacAvaney, Sean  and
      Ireland, Molly  and
      Ophir, Yaakov  and
      Atzil-Slonim, Dana  and
      Varadarajan, Vasudha  and
      Bedrick, Steven  and
      Desmet, Bart",
    booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.clpsych-1.41/",
    pages = "510--520",
    ISBN = "979-8-89176-421-7",
    abstract = "Social media posts are a rich and valuable source of a data to analyze the mental health states and users' well-being using automatic analysis tools. In this work we show, how we used a range of Natural Language Processing (NLP) methods such as Long-Short Term Memory (LSTM), BERT-based models and Large Language Models (LLMs) for self-states and well-being analysis and summarization during the CLPsych Shared Task 2026. Our approach achieved one of the top Consistency and Contradiction scores for summarization task and also middle-level results for the other tasks. By testing and developing such mental health-state estimation systems, we managed to contribute to the improvement of the mental health support systems. We make our code available."
}