Am I hurt?: Evaluating Psychological Pain Detection in Hindi Text using Transformer-based Models

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Kaur, Ravleen; Bhatia, MPS and Kumar, Akshi. 2024. Am I hurt?: Evaluating Psychological Pain Detection in Hindi Text using Transformer-based Models. ACM Transactions on Asian and Low-Resource Language Information Processing, 23(8), 125. ISSN 2375-4699 [Article]

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Official URL: https://dl.acm.org/doi/10.1145/3650206

Abstract or Description

The automated evaluation of pain is critical for developing effective pain management approaches that seek to alleviate while preserving patients’ functioning. Transformer-based models can aid in detecting pain from Hindi text data gathered from social media by leveraging their ability to capture complex language patterns and contextual information. By understanding the nuances and context of Hindi text, transformer models can effectively identify linguistic cues, sentiment and expressions associated with pain enabling the detection and analysis of pain-related content present in social media posts. The purpose of this research is to analyse the feasibility of utilizing NLP techniques to automatically identify pain within Hindi textual data, providing a valuable tool for pain assessment in Hindi-speaking populations. The research showcases the HindiPainNet model, a deep neural network that employs the IndicBERT model, classifying the dataset into two class labels {pain, no_pain} for detecting pain in Hindi textual data. The model is trained and tested using a novel dataset, दर्द-ए-शायरी (pronounced as Dard-e-Shayari) curated using posts from social media platforms. The results demonstrate the model’s effectiveness, achieving an accuracy of 70.5%. This pioneer research highlights the potential of utilizing textual data from diverse sources to identify and understand pain experiences based on psychosocial factors. This research could pave the path for the development of automated pain assessment tools that help medical professionals comprehend and treat pain in Hindi speaking populations. Additionally, it opens avenues to conduct further NLP-based multilingual pain detection research, addressing the needs of diverse language communities.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1145/3650206

Additional Information:

"© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record is available at, https://doi.org/10.1145/3650206."

Keywords:

Pain Detection, social media, word embeddings, transformer-based models, emotional pain

Departments, Centres and Research Units:

Computing

Dates: