tartuNLP/reddit-anhedonia by huggingface-mirror (hf-mirror)
Focusing on the PRIMATE dataset, our study reveals concerns regarding annotation validity, particularly for the lack of interest or pleasure symptom. Through re-annotation by a mental health professional, we introduce finer labels and textual spans as evidence, identifying a notable number of false positives. Our refined annotations offer a higher-quality test set for anhedonia detection. This study underscores the necessity of addressing annotation quality issues in mental health datasets, advocating for improved methodologies to enhance NLP model reliability in mental health assessments. A mental health professional (MHP) read all the posts in the subset and labelled them for the presence of loss of interest or pleasure (anhedonia). The MHP assigned three labels to each post: a) 'mentioned' if the symptom is talked about in the text, but it is not possible to infer its duration or intensity; b) 'answerable' if there is clear evidence of anhedonia; c) 'writer's symptoms' which shows whether the author of the post discusses themselves or a third person. Additionally, the MHP selected the part of the text that supports the positive label.
Collaborative assessment as an intervention in the treatment of mental Illness: a systematic review
HappyDB is a crowd-sourced collection of 100,000 happy moments designed to advance the understanding of happiness through text analysis. The database is publicly available and aims to support research in natural language processing (NLP) and positive psychology. It provides insights into the causes of happiness and suggests sustainable actions for improving well-being.
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