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NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

Computer Science

NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

M. Garg, C. Saxena, et al.

This innovative position paper delves into using Natural Language Processing for causal analysis and perception mining to assess mental health through social media interactions. The research, conducted by Muskan Garg, Chandni Saxena, Usman Naseem, Sohn Sunghwan, and Bonnie J Dorr, advocates for the development of explainable AI models to enhance mental health evaluation methods.

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Playback language: English
Abstract
This position paper proposes using Natural Language Processing (NLP) for causal analysis and perception mining to infer mental health from social media. It argues for more explainable AI models in mental health analysis, focusing on two dimensions: 1) Causal analysis to identify cause-and-effect relationships in user-generated text; and 2) Perception mining to understand psychological perspectives influencing online users' intentions. The paper explores critical areas within NLP, particularly discourse analysis, to advance the development of conversational agents for mental health inference from social media.
Publisher
Not specified in provided text
Published On
Jan 01, 2023
Authors
Muskan Garg, Chandni Saxena, Usman Naseem, Sohn Sunghwan, Bonnie J Dorr
Tags
Natural Language Processing
causal analysis
perception mining
mental health
social media
explainable AI
discourse analysis

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