Interpretability of Fine-grained Classification of Sadness and Depression

• URL: http://arxiv.org/abs/2203.10432v1
• Date: Sun, 20 Mar 2022 02:34:51 GMT
• Title: Interpretability of Fine-grained Classification of Sadness and Depression
• Authors: Tiasa Singha Roy, Priyam Basu, Aman Priyanshu and Rakshit Naidu
• Abstract summary: Depression is a longer term mental illness which impairs social, occupational, and other vital regions of functioning. Most of the open sourced data on the web deal with sadness as a part of depression, as the difference in severity of both is huge. In this paper, we aim to highlight the difference between the two and highlight how interpretable our models are to distinctly label sadness and depression.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While sadness is a human emotion that people experience at certain times throughout their lives, inflicting them with emotional disappointment and pain, depression is a longer term mental illness which impairs social, occupational, and other vital regions of functioning making it a much more serious issue and needs to be catered to at the earliest. NLP techniques can be utilized for the detection and subsequent diagnosis of these emotions. Most of the open sourced data on the web deal with sadness as a part of depression, as an emotion even though the difference in severity of both is huge. Thus, we create our own novel dataset illustrating the difference between the two. In this paper, we aim to highlight the difference between the two and highlight how interpretable our models are to distinctly label sadness and depression. Due to the sensitive nature of such information, privacy measures need to be taken for handling and training of such data. Hence, we also explore the effect of Federated Learning (FL) on contextualised language models.

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