A Cross-Modal Study of Pain Across Communities in the United States

Aggarwal, Arnav; Rai, Sunny; Giorgi, Salvatore; Havaldar, Shreya; Sherman, Garrick; Mittal, Juhi; Guntuku, Sharath Chandra

doi:10.1145/3543873.3587642

Companion Proceedings of the ACM Web Conference 2023 2023

DOI: 10.1145/3543873.3587642

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A Cross-Modal Study of Pain Across Communities in the United States

Arnav Aggarwal

Sunny Rai

Salvatore Giorgi

et al.

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2024

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Am I Hurt?: Evaluating Psychological Pain Detection in Hindi Text Using Transformer-based Models

Kaur,

Bhatia,

Kumar

2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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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.

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Am I Hurt?: Evaluating Psychological Pain Detection in Hindi Text Using Transformer-based Models

Kaur,

Bhatia,

Kumar

2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

View full text Add to dashboard Cite

show abstract

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A Cross-Modal Study of Pain Across Communities in the United States

Cited by 1 publication

References 46 publications

Am I Hurt?: Evaluating Psychological Pain Detection in Hindi Text Using Transformer-based Models

Am I Hurt?: Evaluating Psychological Pain Detection in Hindi Text Using Transformer-based Models

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