IMF in E :An Integrated BERT - CNN - B i GRU Model for Mental Health Detection in Financial Context on Textual Data (2022)

Abstract: Nowadays, mental health is a global issue. It is a pervasive phenomenon over online social network platforms. It is observed in varied categories, such as depression, suicide, and stress on the Web. Hence, mental health detection problem is receiving continuous attention among computational linguistics researchers. On the other hand, public emotions and reactions play a significant role in financial domain and the issue of mental health is directly associated. In this paper, we propose a new study to detect mental health in financial context. It starts with two-step data filtration steps to prepare the mental health dataset in financial context. A new model called IMFinE is introduced. It consists of an input layer, followed by two relevant BERT embedding layers, a convolutional neural network, a bidirectional gated recurrent unit, and finally, dense and output layers. The empirical evaluation of the proposed model is performed on Reddit datasets and it shows impressive results in terms of precision, recall, and f-score. It also outperforms relevant state-of-the-art and baseline methods. To the best of our knowledge, this is the first study on mental health detection in financial context.

## introduction the popularity of online social network (osn) platforms, such as twitter, facebook, and reddit have been growing at an unprecedented rate @xcite . they have become a real-time and large-scale communication source to find and connect with users across the globe @xcite . these platforms offer users to express their moods, emotions, sentiments, and views. besides that, users are involved in several activities like exchanging messages with friends, participating in ongoing trends, and establishing connection with celebrities. hence, an enormous amount of data is generated from these online platforms @xcite . it contains rich semantic information which can be used for several applications, such as sentiment analysis and opinion mining, predictive modeling, web surveillance, recommendation systems, information retrieval, data summarization, and cyber-security @xcite . in addition, it is also beneficial for several interdisciplinary studies including psychology, behavioral, and cognitive @xcite . in the last few years, it is seen that users consider osns to address mental health problems. especially, after the occurrence of the coronavirus disease (covid-19) pandemic, which has affected us at physical-, mental-, and psychological-level. the number of cases related to mental health problems, such as depression, stress, anxiety, and suicide have surged. as a result, it is a briefly discussed topic world-wide and rapidly increasing across osns in the form of tweets, posts, comments, and blogs. in the latest survey, the rate of increase of such mental health problems is found larger as compared to the physical health impacts in china @xcite . also, it is seen that around 80% of people reveal their intention of committing suicide on social media platforms @xcite . depression causes frequent stress and it occurs due to many reasons, such as stressful life events including bereavement, divorce, prolonged illness, or financial issues. further, prevailing it for a long span of time develops suicidal tendencies @xcite . recently, people have been taking osn platforms to communicate and receive advice on mental health-related issues. it has also motivated computational linguistic researchers in the sense that information mining from massive amount of user-generated contents (ugcs) can be used in mental health identification and detection. in this line, a wide range of natural language processing (nlp) and data mining approaches and techniques are applied along with handcrafted features for mental health classification on ugcs. therefore, several traditional machine learning (ml) techniques and advanced deep learning (dl) models are taken into consideration for mental health classification @xcite . ## related work this section presents the existing sota related to mental health identification and detection on osn platforms. besides that, this section also highlights the current status and limitations in the end. @xcite analyzed the language which indicates depressive moods on twitter data. they considered depressive attitudes and actions of users, organized and conducted interviews between two users, and analyzed the correlation between interviews and data available on twitter. @xcite highlighted "major depressive disorders" prediction and considered many behavioral patterns of depressed users from this social media data. @xcite considered users behavior to predict depression on twitter in japanese language. ronghua and qingpeng (2016) explained that users reveal their moods related to depression analysis via social media. @xcite introduced six depression-based feature groups. they proposed a multi-modal depressive dictionary learning model for depressed user detection on twitter. @xcite identified suicide and depression via dl techniques. @xcite proposed stress detection on twitter. they analyzed psychological pressures in teenagers' tweets. @xcite extracted semantic features and combined multi-task learning using cnn for identification of stress related topics and events on social media data. @xcite introduced a rule-based approach to analyze stress and relaxation using both direct and indirect expressions on twitter. @xcite considered reddit and proposed a dataset namely, dreadit for stress classification. they applied a manual approach to construct this dataset. @xcite considered posts by users before the suicide attempt on twitter and analyzed the lexical markers and emotions in it. @xcite considered facial clues from the recorded video for stress and anxiety detection using ml techniques. @xcite presented stress detection using dl techniques. they applied cnn and multi-layer perceptron. @xcite proposed a new dataset for stress detection on twitter and reddit datasets. all aforementioned literature presents the availability of the datasets across osn platforms and highlights important insights for mental health detection in terms of depression, suicide, and stress. however, there is no literature that addresses mental health detection in financial context using textual data, which is a challenging, worth, and significant research investigation task. to the best of our knowledge, this is the first study on mental health detection in financial context on textual data via dl-based models. ## problem description and dataset preparation this section presents the problem description for mental health detection in finance and covers dataset preparation. ## proposed model this section presents a detailed description of the proposed imfine model. it includes an input layer, two embeddings (mentalbert and finbert), cnn, bigru, followed by a dense and output layers. figure 4 presents the architecture of the proposed imfine model. ## experimental setup and results this section presents the experimental details of the proposed imfine model. it includes the statistical details of the datasets, experimental and hyper-parameter settings, evaluation metrics, followed by evaluation results and comparative analysis, and ablation study.