Emotion-Infused Models for Explainable Psychological Stress Detection (2021)

Abstract: The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital. Here, we present work exploring the use of a semantically related task, emotion detection, for equally competent but more explainable and human-like psychological stress detection as compared to a black-box model. In particular, we explore the use of multi-task learning as well as emotion-based language model fine-tuning. With our emotion-infused models, we see comparable results to state-of-the-art BERT. Our analysis of the words used for prediction show that our emotion-infused models mirror psychological components of stress.

## introduction as crises have begun to multiply worldwide, including the covid-19 pandemic and the resulting economic downturn, psychological stress has risen dramatically 1 . the problem of detecting psychological stress, and more broadly, of detecting people in distress and in need of help, is a sensitive application; therefore, the ability to interpret the results, in order to understand why, is vital. the consequences of blindly trusting a black-box model and mislabeling users' stress levels could be serious in a deployed application such as a therapeutic chatbot, where some users may not receive the immediate help they need. furthermore, models that make decisions based on psychology theory about factors that impact stress will be easier for humans to understand, and their mistakes will be more obvious. researchers have recently begun to study psychological stress, but in this work, we propose a new focus on examining the information our models use to make decisions and finding ways to incorporate psychological factors, like emotion, into them. traditional multi-task learning would normally be helpful in this situation, but there are no currently available datasets labeled with both stress and emotion. even if there were, it would be beneficial to incorporate external information without re-labeling new datasets for each new combination of useful tasks. here, we present work exploring how to use semantically related tasks-here, emotion detection-to create emotion-infused models capable of equally competent, but explainable, psychological stress detection as compared to a blackbox model. in particular, we explore the use of multi-task learning as well as emotion-infused language model fine-tuning, two existing frameworks which we examine through the lens of interpetability. our code for this work is available at github. com/eturcan/emotion-infused. our contributions in this work are as follows: (i) consideration of factors suggested by psychological theory in deep learning methods for predicting stress, with a focus on emotion; (ii) an exploration of three different approaches to emotion-infused models, with experimental results showing comparable results to the state-of-the-art in all cases; and (iii) a framework for interpreting our models to show the impact of emotion and other factors in our models. ## related work researchers who use natural language approaches for stress detection often rely on external resources such as diagnostic questionnaires (e.g., @xcite ) or techniques like pattern matching (patterns such as "i am stressed", e.g., @xcite ; @xcite ) to assign labels. much of the work that has been done on psychological stress detection focuses either on establishing baseline models with little advancement in computational modeling, or on using external information about the text (e.g., author, time of posting, number of replies), which is usually, but not always available and may differ in meaning or importance across platforms and domains. there has also been a substantial amount of work on detecting related mental health concerns such as anxiety (e.g., @xcite ; @xcite ; @xcite al. (2020) ), but these are distinct from the generalized experience of stress. the most similar work to ours is @xcite , our prior work publishing a dataset of psychological stress collected from the social media website reddit and labeled by crowd workers, and presenting baselines with several basic non-neural and bert-based models on this data. we use this dataset in our current work; however, we focus on exploring interpretable frameworks for this sensitive task and connecting the stress detection task concretely with emotion detection. the models we propose in this work rely on two types of enhancements to the neural representation learned by models like bert: multi-task learning and pre-training or fine-tuning. multi-task learning is an increasingly popular framework in which some parameters in a model are shared between or used to inform multiple different tasks. hard parameter sharing @xcite , the variant we employ, uses some set of parameters as a shared base representation and then allows each task to have some private parameters on top and perform their own separate predictions. multi-task learning has been successfully applied to many domains across nlp @xcite @xcite ; we are especially interested in instances where it has improved semantic and emotion-related tasks, such as @xcite , who perform emotion detection with a suite of secondary semantic tasks including personality classification. pre-training and fine-tuning are another type of transfer learning where multiple tasks are trained in sequence rather than at the same time. pre-trained language models are perhaps the most widely used example, where a large neural language model can dataset size dreaddit 3,553 goemotions a,e,s 58k goemotions f sj 4,136 vent 1.6m it should be noted that the psychological stress is much better studied in settings where researchers have access to some physiological signals (e.g., zuo et al. (2012 2020 ). this work is not as relevant to our task, since we have only text data available when detecting stress from online posts. ## data a comparison of all the datasets we use in this work can be seen in because this dataset is small for training a deep learning model, we also experiment with larger datasets to provide auxiliary information. we se-lect the goemotions dataset @xcite , which consists of 58,009 reddit comments labeled by crowd workers with one or more of 27 emotions (or neutral), for its larger size and genre similarity to dreaddit. in this paper, we refer to the dataset in this form as goemotions all or goemotions a . the authors also published two relabelings of this dataset, achieved by agglomerative clustering: one where labels are clustered together into the ekman 6 basic emotions (anger, disgust, fear, joy, sadness, surprise, neutral) (ekman, 1992) (goemotions ekman/e ), and one into simple polarity (positive, negative, ambiguous, neutral) (goemotions sentiment/s ). we run our experiments with each version of this dataset. we also explore the use of another social media website, vent. vent is a platform more similar to twitter or tumblr than reddit, where users post vents of any length and tag them as they like, and other users react to them or post comments. the benefit of vent for this purpose is that posters selfidentify some emotion they are feeling from a large list of pre-made emotions. the data we use is collected by @xcite foot_0 . we select vent data that has been labeled with fear or sadness, which we hypothesize to be related to stress, as well as joy, for a contrast. we note that this dataset is strictly single-class, whereas goemotions may have more than one emotion label per data point. in all, there are 1.6m vents in our dataset, much larger than dreaddit or goemotions; we randomly sample this data in a stratified manner to create a training, development, and test set with an 80/10/10 ratio. to examine the effects of domain similarity, we also select a subset of goemotions with the corresponding emotion labels -we subsample the existing "all" dataset to select only data points originally labeled with fear, joy, or sadness, for a final set of 4,136 data points (3,342 of which are the train set). we call this subset goemotions f sj , and we compare it against vent to see whether genre similarity or data size is more important in this multitask setting. ## models we experiment with three types of emotion-infused models; that is, we present three different ways to incorporate emotion information into our stress detection models, divided into multi-task learning and fine-tuning. ## analysis we perform three different analyses to probe our trained models and discover what information they learn to use. for our multi alt models, we investigate the usefulness of the emotion prediction layers in explaining stress classifications, and for all models, we use local interpretable model-agnostic explanations (lime) @xcite to show that our emotion-infused models rely on meaningfully different types of words than bert in order to make their predictions. ## conclusion in this work, we present a suite of emotionenhanced models that incorporate emotional information in various ways to enhance the task of binary stress prediction. all three types of our models achieve comparable performance to a state-of-theart fine-tuning bert baseline, and, more importantly, we show that they result in more explainable models. we also introduce a new framework for model interpretation using lime and show that our emotion-enhanced multi-task models offer a new dimension of interpetability by using the predictions of auxiliary tasks to explain the primary task. in our future work, we hope to expand these analyses to tasks in other domains and devise model architectures that can make more direct use of multitask learning to make and explain their predictions.