@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T psytechlab at CLPsych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis
%A Buyanov, Igor
%A Valieva, Nafisa
%A Mazurina, Ekaterina
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F buyanov-etal-2026-psytechlab
%X 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.
%U https://aclanthology.org/2026.clpsych-1.41/
%P 510-520
Markdown (Informal)
[psytechlab at CLPsych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis](https://aclanthology.org/2026.clpsych-1.41/) (Buyanov et al., CLPsych 2026)
ACL