With the rapid increase in the complicated nature of mental disorders, recognizing mental illness has become a huge concern in the real world. Depression often causes different psychological, physical, or anxiety disorders due to the mental illness heavily affecting the behavior of the people. Clinical depression increases the disability, and reduced functionality also leads to suicide due to the depression.
Nowadays, social media information provides potential information regarding mental behavior due to the frequent interaction of the users with their social media platforms in their daily life. Hence, the depression detection system potentially utilizes the information expressed in social media in the form of text, images, audio, or video to determine the mental state of the social users. Even though the depression diagnosis systems have proven an effective treatment for depressed patients, misdiagnosing the people is critical due to the lack of intelligent systems and resources.
Most depression detection models analyze the textual content in the social media posts too early to detect the depression level. Owing to the increased uncertainties in people’s feelings, the depression detection system encounters challenges in recognizing the risk level of depression.
Various data mining, machine learning, and deep learning models have been adopted to detect depression from any single modality or multiple modalities. Deep neural networks, such as the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM) play an imperative role in determining the depression from any structured or unstructured data of text, image, speech, or /and video.
Depression or anxiety detection systems have been increasingly used in medical applications during surgery, exam or interview processes, suicide prevention, death rate mitigation, and primary care of the child, adult, or women against the unknown threatening or mental illness.
In the field of depression detection, the researchers have been focused on different categories of depression detection. Recently, emerging depression detection research areas have been presented as follows.
Depressive Text Recognition:
Depression detection from the inherent analysis of the linguistic characteristics of the user-generated text has become an emerging research area due to people expressing their opinions and emotions in the form of natural language texts during conversation and social media posts.
Depressive Speech Recognition:
The analysis of the speech patterns of the people assists in recognizing the risk of depression through acoustic feature extraction regarding their mental state. Hence, the intelligent analysis of the speech signals enforces the early diagnosis of mental illness patients.
Depressive Eye Movement Recognition:
Eye movement and blinking become the major bio-markers of detecting depression in the human body. The physiological analysis of the eye movements indicates the severity or progress of the depression based on the rapid eye movements through deep feature learning.
The Depression detection system examines multiple modalities such as the text, image, speech, and video to detect depression risk levels. By recognizing emotions from multiple modalities, the depression detection system ignores the capturing of fake emotions in a single modality through interlinking multiple emotions.
In depression detection, suicide ideation detection determines whether an individual has suicidal thoughts or not from their generated textual content. Hence, mining the social content from the online communities assists in suicide prevention due to the rapidly increasing interactions and expressiveness of the feelings, like suicidal tendencies in the online communication channels.
Postpartum depression detection is one of the common medical complications during childbearing. The screening of the patients involves the analysis of their previous depression, family history in the perspective of depression, depression during pregnancy, stressed life events, and immigrant status features to leverage the prompt recognition of the postpartum depression.
Depression detection models still confront several shortcomings in their research area, which are discussed.
Manually detecting the depression patterns from the unimodal or multimodal features and early diagnosing the depressed patients is challenging due to the inherent relationships among the emotions and expressed patterns.
Due to the lack of labeled data in the social media content, discriminating the depressed and non-depressed users or categorizing the risk levels of the depression is infeasible by the supervised learning model.
In online communities, few users express their feelings or emotions with brief textual content, which tends to recognize the depressive tendency inaccurately.
Analyzing the log activities, textual content, or demographic features of the users limits the assessment of the depression behavior due to the diverse expressiveness of the people on multiple modalities such as emojis, audio, images, and videos.
The lack of analyzing the historical information of the users in a temporal context in the depression detection system negatively impacts the capturing of the changes in the users’ behavior patterns over time.
Cross-domain Depression Detection in Social Media:
• To develop the depression detection system from the analysis of the social media, such as the explicit and implicit behaviors of the social users
• To enhance depression detection with the knowledge of the information from the external domain sources
Emotional Feature Extraction in Depression Detection:
• To detect the depression from the natural language text data through potential feature extraction
• To extract the depressive features to perform the emotional analysis on the social media text data
Multimodal Extraction for Depression Detection:
• To design the depression detection from the knowledge of the users’ expressed information in multiple modalities
• To extract the inherent emotions in the inter-linked multiple modalities of text, image, and speech to recognize the depression of the user
Early Depression Detection with Deep Attention Network:
• To investigate the sequence of user-generated text to detect the depression patterns early
• To design a deep attentive network for the learning of the user-generated content to detect the depression
Context Vector Representation of Text Sequence for Depression Detection:
• To examine the text sequence of the user-generated content to provide the imperative feature to the learning model
• To contextually transform the natural language text into the vector representation to build the context-aware depression detection
Aspect based Depressive Feature Extraction for Multi-Class Depression Classification:
• To develop a depression detection system to recognize the multiple levels of the depressed users
• To analyze the aspect of the users’ expressed natural language text to extract the features for the depression detection
Environmental Data-Aware Behavior Modeling and Depression Detection:
• To build the depression detection model with the influence of the contextual behavior modeling
• To investigate the environmental factors that impact the user behaviors to detect depression.