Fatima, Asra, Li, Ying, Hills, Thomas Trenholm and Stella, Massimo (2021) DASentimental : detecting depression, anxiety, and stress in texts via emotional recall, cognitive networks, and machine learning. Big Data and Cognitive Computing, 5 (4). e77. doi:10.3390/bdcc5040077 ISSN 2504-2289.

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Abstract

Most current affect scales and sentiment analysis on written text focus on quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions of similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, to extract depression, anxiety, and stress from written text. We trained DASentimental to identify how N = 200 sequences of recalled emotional words correlate with recallers’ depression, anxiety, and stress from the Depression Anxiety Stress Scale (DASS-21). Using cognitive network science, we modeled every recall list as a bag-of-words (BOW) vector and as a walk over a network representation of semantic memory—in this case, free associations. This weights BOW entries according to their centrality (degree) in semantic memory and informs recalls using semantic network distances, thus embedding recalls in a cognitive representation. This embedding translated into state-of-the-art, cross-validated predictions for depression (R = 0.7), anxiety (R = 0.44), and stress (R = 0.52), equivalent to previous results employing additional human data. Powered by a multilayer perceptron neural network, DASentimental opens the door to probing the semantic organizations of emotional distress. We found that semantic distances between recalls (i.e., walk coverage), was key for estimating depression levels but redundant for anxiety and stress levels. Semantic distances from “fear” boosted anxiety predictions but were redundant when the “sad−happy” dyad was considered. We applied DASentimental to a clinical dataset of 142 suicide notes and found that the predicted depression and anxiety levels (high/low) corresponded to differences in valence and arousal as expected from a circumplex model of affect. We discuss key directions for future research enabled by artificial intelligence detecting stress, anxiety, and depression in texts.

Item Type:	Journal Article
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science, Engineering and Medicine > Science > Physics
SWORD Depositor:	Library Publications Router
Library of Congress Subject Headings (LCSH):	Cognitive science, Cognitive science -- Data processing, Text data mining , Computational linguistics , Natural language processing (Computer science)., Artificial intelligence
Journal or Publication Title:	Big Data and Cognitive Computing
Publisher:	MDPI
ISSN:	2504-2289
Official Date:	13 December 2021
Dates:	Date Event 13 December 2021 Published 6 December 2021 Accepted
Volume:	5
Number:	4
Article Number:	e77
DOI:	10.3390/bdcc5040077
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	7 February 2022
Date of first compliant Open Access:	8 February 2022

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