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Article

DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning

1
CogNosco Lab, Department of Computer Science, University of Exeter, Exeter EX4 4PY, UK
2
Center for Adaptive Rationality, Max Planck Institute for Human Development, 14195 Berlin, Germany
3
Department of Psychology, University of Warwick, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Tzung-Pei Hong
Big Data Cogn. Comput. 2021, 5(4), 77; https://doi.org/10.3390/bdcc5040077
Received: 26 October 2021 / Revised: 22 November 2021 / Accepted: 6 December 2021 / Published: 13 December 2021
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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. View Full-Text
Keywords: cognitive network science; text analysis; natural language processing; artificial intelligence; emotional recall; cognitive data; AI cognitive network science; text analysis; natural language processing; artificial intelligence; emotional recall; cognitive data; AI
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MDPI and ACS Style

Fatima, A.; Li, Y.; Hills, T.T.; Stella, M. DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning. Big Data Cogn. Comput. 2021, 5, 77. https://doi.org/10.3390/bdcc5040077

AMA Style

Fatima A, Li Y, Hills TT, Stella M. DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning. Big Data and Cognitive Computing. 2021; 5(4):77. https://doi.org/10.3390/bdcc5040077

Chicago/Turabian Style

Fatima, Asra, Ying Li, Thomas Trenholm Hills, and Massimo Stella. 2021. "DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning" Big Data and Cognitive Computing 5, no. 4: 77. https://doi.org/10.3390/bdcc5040077

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